################################################################################################################## ################################################################################################################## ######################################################2018######################################################## ################################################################################################################## ################################################################################################################## ################################################ #############PREPARING THE DATA################# ################################################ library(foreign) library(memisc) rm (list=ls()) #AB5 AB5 <- read.spss("C:/Uni/Data/AB wave 5.sav",use.value.labels=T, to.data.frame = T) #AB5 <- read.spss("C:/Users/u265173/OneDrive/AB v WVS w Veronica/AB wave 5.sav",use.value.labels=T, to.data.frame = T) table(AB5$country) dmAB5 <- subset(AB5, AB5$country== "Egypt" | AB5$country == "Iraq" | AB5$country == "Jordan" | AB5$country == "Lebanon" | AB5$country == "Tunisia") dmAB5$country<-droplevels(dmAB5$country) table(dmAB5$country) dmAB5$Surveytype <- 1 table(dmAB5$Surveytype) table(dmAB5$country) #12601 in AB. #WVS7 WV7 <- read.spss("C:/Uni/Data/WVS wave 7.sav",use.value.labels=T, to.data.frame = T) #WV7 <- read.spss("C:/Users/u265173/OneDrive/AB v WVS w Veronica/WVS wave 7.sav",use.value.labels=T, to.data.frame = T) table(WV7$B_COUNTRY) dmWV7 <- subset(WV7, WV7$B_COUNTRY=="Egypt" | WV7$B_COUNTRY=="Iraq"| WV7$B_COUNTRY=="Jordan"| WV7$B_COUNTRY=="Lebanon"| WV7$B_COUNTRY== "Tunisia") dmWV7$B_COUNTRY <- droplevels(dmWV7$B_COUNTRY) table(dmWV7$B_COUNTRY) dmWV7$Surveytype <- 0 table(dmWV7$Surveytype) table(dmWV7$B_COUNTRY) #6,011 in WVS. ################################################################################################################## ################################################################################################################## ##############################################PREPARING ATTITUDES################################################# ################################################################################################################## ################################################################################################################## ############################################################### #################VARIABLE 1: GENERALIZED TRUST################# ############################################################### ######trust AB3 #Generally speaking, would you say that most people can be trusted? #1 = Most people can be trusted #2 = You must be very careful in dealing with people #98 = Cant Choose/Don't know #99 = Decline to Answer summary(dmAB5$Q103) dmAB5$trustab <- as.numeric(dmAB5$Q103) table(dmAB5$trustab) dmAB5$Trust[dmAB5$trustab == 1] <- 1 dmAB5$Trust[dmAB5$trustab == 2] <- 0 dmAB5$Trust[dmAB5$trustab == 3] <- NA dmAB5$Trust[dmAB5$trustab == 4] <- NA table(dmAB5$Trust) table(dmAB5$Trust, dmAB5$Q103) # dmAB5$Trust: 0 - Most people are not trustworthy # 1 - Most people are trustworthy ######trust WV6 #Generally speaking, would you say that most people #can be trusted or that you need to be very careful in dealing with people? #Most people can be trusted #Need to be very careful #No answer -> NA #Don't know -> NA summary(dmWV7$Q57) dmWV7$trustwvs <- as.numeric(dmWV7$Q57) table(dmWV7$trustwvs) dmWV7$Trust[dmWV7$trustwvs == 1] <- 1 dmWV7$Trust[dmWV7$trustwvs == 2] <- 0 table(dmWV7$Trust) #0 - Need to be very careful # 1 - Most people can be trusted ############################################################### #################VARIABLE 2: EDUCATIONAL GE#################### ############################################################### #AB#: q6014: A university education is more important #for a boy than a girl summary(dmAB5$Q601_4) dmAB5$unieducab <- as.numeric(dmAB5$Q601_4) table(dmAB5$unieducab) dmAB5$Unieduc[dmAB5$unieducab == 1] <- 0 dmAB5$Unieduc[dmAB5$unieducab == 2] <- 0.33 dmAB5$Unieduc[dmAB5$unieducab == 3] <- 0.67 dmAB5$Unieduc[dmAB5$unieducab == 4] <- 1 dmAB5$Unieduc[dmAB5$unieducab == 5] <- NA dmAB5$Unieduc[dmAB5$unieducab == 6] <- NA dmAB5$Unieduc[dmAB5$unieducab == 7] <- NA table(dmAB5$Unieduc) # Unieduc: 0 - Strongly agree, 1 - Strongly disagree #WVS6 educge. #V52: A university education is more #important for a boy than for a girl summary(dmWV7$Q30) dmWV7$Unieduc <- as.numeric(dmWV7$Q30) table(dmWV7$Unieduc) dmWV7$Unieduc[dmWV7$Unieduc == 1] <- 0 dmWV7$Unieduc[dmWV7$Unieduc == 2] <- 0.33 dmWV7$Unieduc[dmWV7$Unieduc == 3] <- 0.67 dmWV7$Unieduc[dmWV7$Unieduc == 4] <- 1 table(dmWV7$Unieduc) ############################################################### #################VARIABLE 3: POLITICAL GE###################### ############################################################### #AB3 #q6013: On the whole, men make better political leaders than women do summary(dmAB5$Q601_3) dmAB5$polleadab <- as.numeric(dmAB5$Q601_3) table(dmAB5$polleadab) dmAB5$PolLead[dmAB5$polleadab == 1] <- 0 dmAB5$PolLead[dmAB5$polleadab == 2] <- 0.33 dmAB5$PolLead[dmAB5$polleadab == 3] <- 0.67 dmAB5$PolLead[dmAB5$polleadab == 4] <- 1 dmAB5$PolLead[dmAB5$polleadab == 5] <- NA dmAB5$PolLead[dmAB5$polleadab == 6] <- NA dmAB5$PolLead[dmAB5$polleadab == 7] <- NA table(dmAB5$PolLead) #WVS6 #political GE wvs6. #V51: On the whole, men make better political leaders than women do #V51 summary(dmWV7$Q29) dmWV7$poleducwvs <- as.numeric(dmWV7$Q29) table(dmWV7$poleduc) dmWV7$PolLead[dmWV7$poleducwvs == 1] <- 0 dmWV7$PolLead[dmWV7$poleducwvs == 2] <- 0.33 dmWV7$PolLead[dmWV7$poleducwvs == 3] <- 0.67 dmWV7$PolLead[dmWV7$poleducwvs == 4] <- 1 table(dmWV7$PolLead) ############################################################### ##############VARIABLE 4: TRUST IN POLICE###################### ############################################################### #AB3 201: 4. #I will name a number of institutions, and I would like you to tell me to #what extent you trust each of them: #public security(the police) summary(dmAB5$Q201A_42) dmAB5$Trustpol[dmAB5$Q201A_42 == "great trust"] <- 1 dmAB5$Trustpol[dmAB5$Q201A_42 == "some trust"] <- 0.67 dmAB5$Trustpol[dmAB5$Q201A_42 == "little trust"] <- 0.33 dmAB5$Trustpol[dmAB5$Q201A_42 == "no trust"] <- 0 dmAB5$Trustpol[dmAB5$Q201A_42 == "don't know"] <- NA dmAB5$Trustpol[dmAB5$Q201A_42 == "refused"] <- NA #dmAB5$Trustpol <-recode(as.numeric(dmAB5$Q201A_42), "1=1; 2=0.67; 3=0.33; 4=0; 5=NA; 6=NA") table(dmAB5$Trustpol,dmAB5$Q201A_42) table(dmAB5$Trustpol) #0 - don't trust; 1 - trust to great extent #WVS6 #ConfidencE: The police summary(dmWV7$Q69) dmWV7$Trustpol[dmWV7$Q69 == "A great deal"] <- 1 dmWV7$Trustpol[dmWV7$Q69 == "Quite a lot"] <- 0.67 dmWV7$Trustpol[dmWV7$Q69 == "Not very much"] <- 0.33 dmWV7$Trustpol[dmWV7$Q69 == "None at all"] <- 0 table(dmWV7$Trustpol) #0 - no trust at all; 1 - trust to great extent table(dmWV7$Trustpol, dmWV7$country) #not in egypt. ################################################################################################################## ################################################################################################################## ##############################################PREPARING DEMOGRAPHICS############################################## ################################################################################################################## ################################################################################################################## ################################################ #################COUNTRY######################## ################################################ table(dmAB5$country) dmWV7$country <- as.factor(dmWV7$B_COUNTRY) table (dmAB5$country) table (dmWV7$country) ################################################ #################GENDER######################### ################################################ table(dmAB5$Q1002) dmAB5$gender[dmAB5$Q1002 == "male"] <- 0 dmAB5$gender[dmAB5$Q1002 == "female"] <- 1 table(dmAB5$Q1002, dmAB5$gender) #0 - male, 1 - female table(dmWV7$Q260) dmWV7$gender[dmWV7$Q260 == "Male"] <- 0 dmWV7$gender[dmWV7$Q260 == "Female"] <- 1 table(dmWV7$Q260, dmWV7$gender) ################################################ ###################AGE########################## ################################################ summary(dmWV7$Q262) table(dmWV7$Q262) #this one is not numeric. dmWV7$age_short <- as.numeric(paste(dmWV7$Q262)) summary(dmWV7$age_short) table(dmWV7$age_short) dmWV7$agecats[dmWV7$age_short < 30] <- 0 dmWV7$agecats[29 < dmWV7$age_short & dmWV7$age_short < 50] <- 1 dmWV7$agecats[dmWV7$age_short > 49] <- 2 table(dmWV7$agecats) table(dmWV7$age_short, dmWV7$agecats) summary(dmAB5$Q1001) table(dmAB5$Q1001) #this one is numeric. dmAB5$age_short <- dmAB5$Q1001 dmAB5$age_short[dmAB5$Q1001 == 99999] <- NA summary(dmAB5$age_short) dmAB5$agecats[dmAB5$age_short < 30] <- 0 dmAB5$agecats[29 < dmAB5$age_short & dmAB5$age_short < 50] <- 1 dmAB5$agecats[dmAB5$age_short > 49] <- 2 table(dmAB5$agecats) table(dmAB5$age_short, dmAB5$agecats) #remove.packages("rlang") #remove.packages("dplyr") #install.packages("rlang") #install.packages("dplyr") library(dplyr) ################################################ ###################EDUCATION#################### ################################################ table(dmAB5$Q1003) table(dmAB5$Q1003, dmAB5$country) dmAB5$education <- as.numeric(dmAB5$Q1003) table(dmAB5$education) dmAB5$Educ[dmAB5$education == 1] <- 0 dmAB5$Educ[dmAB5$education == 2] <- 1 dmAB5$Educ[dmAB5$education == 3] <- 1 dmAB5$Educ[dmAB5$education == 4] <- 2 dmAB5$Educ[dmAB5$education == 5] <- 3 dmAB5$Educ[dmAB5$education == 6] <- 3 dmAB5$Educ[dmAB5$education == 7] <- 3 dmAB5$Educ[dmAB5$education == 8] <- NA dmAB5$Educ[dmAB5$education == 9] <- NA table(dmAB5$Educ) summary(dmAB5$Educ) table(dmAB5$country, dmAB5$Educ) # 0 - No, 1 - Primary, 2 - Secondary, 3 - Tertiary dmAB5$Educcats[dmAB5$Educ < 3] <- 0 dmAB5$Educcats[dmAB5$Educ == 3] <- 1 table(dmAB5$Educcats, dmAB5$Educ) table(dmWV7$Q275) dmWV7$educ <- as.numeric(dmWV7$Q275) table(dmWV7$educ) dmWV7$Educ[dmWV7$educ == 1] <- 0 dmWV7$Educ[dmWV7$educ == 2] <- 1 dmWV7$Educ[dmWV7$educ == 3] <- 2 dmWV7$Educ[dmWV7$educ == 4] <- 2 dmWV7$Educ[dmWV7$educ == 5] <- 2 dmWV7$Educ[dmWV7$educ == 6] <- 3 dmWV7$Educ[dmWV7$educ == 7] <- 3 dmWV7$Educ[dmWV7$educ == 8] <- 3 dmWV7$Educ[dmWV7$educ == 9] <- 3 table(dmWV7$Educ) summary(dmWV7$Educ) # 0 - No, 1 - Primary, 2 - Secondary, 3 - Tertiary dmWV7$Educcats[dmWV7$Educ < 3] <- 0 dmWV7$Educcats[dmWV7$Educ == 3] <- 1 table(dmWV7$Educcats, dmWV7$Educ) ################################################ ###################MARITAL STATUS############### ################################################ table(dmAB5$Q1010) dmAB5$marit <- as.numeric(dmAB5$Q1010) table(dmAB5$marit) dmAB5$Mar[dmAB5$marit == 1] <- 1 dmAB5$Mar[dmAB5$marit == 2] <- 2 dmAB5$Mar[dmAB5$marit == 3] <- 2 dmAB5$Mar[dmAB5$marit == 4] <- 0 dmAB5$Mar[dmAB5$marit == 5] <- 2 dmAB5$Mar[dmAB5$marit == 6] <- 2 dmAB5$Mar[dmAB5$marit == 7] <- 2 dmAB5$Mar[dmAB5$marit == 8] <- NA dmAB5$Mar[dmAB5$marit == 9] <- NA table(dmAB5$Mar) dmAB5$singled[dmAB5$Mar == 1] <- 1 dmAB5$singled[dmAB5$Mar == 0] <- 0 dmAB5$singled[dmAB5$Mar == 2] <- 0 dmAB5$othermaritald[dmAB5$Mar == 2] <- 1 dmAB5$othermaritald[dmAB5$Mar == 0] <- 0 dmAB5$othermaritald[dmAB5$Mar == 1] <- 0 table(dmAB5$singled) table(dmAB5$othermaritald) # 0 - married, 1 - single, 2 other table(dmWV7$Q273) dmWV7$marit <- as.numeric(dmWV7$Q273) table(dmWV7$marit) dmWV7$Mar[dmWV7$marit == 1] <- 0 dmWV7$Mar[dmWV7$marit == 2] <- 2 dmWV7$Mar[dmWV7$marit == 3] <- 2 dmWV7$Mar[dmWV7$marit == 4] <- 2 dmWV7$Mar[dmWV7$marit == 5] <- 2 dmWV7$Mar[dmWV7$marit == 6] <- 1 table(dmWV7$Mar) dmWV7$singled[dmWV7$Mar == 1] <- 1 dmWV7$singled[dmWV7$Mar == 0] <- 0 dmWV7$singled[dmWV7$Mar == 2] <- 0 dmWV7$othermaritald[dmWV7$Mar == 2] <- 1 dmWV7$othermaritald[dmWV7$Mar == 0] <- 0 dmWV7$othermaritald[dmWV7$Mar == 1] <- 0 table(dmWV7$singled) table(dmWV7$othermaritald) # 0 - married, 1 - single, 2 other ################################################ #################EMPLOYMENT STATUS############## ################################################ table(dmAB5$Q1005) dmAB5$empl <- as.numeric(dmAB5$Q1005) table(dmAB5$empl) dmAB5$Empl[dmAB5$empl == 1] <- 1 dmAB5$Empl[dmAB5$empl == 2] <- 1 dmAB5$Empl[dmAB5$empl == 3] <- 0 dmAB5$Empl[dmAB5$empl == 4] <- 0 dmAB5$Empl[dmAB5$empl == 5] <- 0 dmAB5$Empl[dmAB5$empl == 6] <- 0 dmAB5$Empl[dmAB5$empl == 7] <- 0 dmAB5$Empl[dmAB5$empl == 8] <- NA dmAB5$Empl[dmAB5$empl == 9] <- NA table(dmAB5$Empl) table(dmWV7$Q279) dmWV7$empl <- as.numeric(dmWV7$Q279) table(dmWV7$empl) dmWV7$Empl[dmWV7$empl == 1] <- 1 dmWV7$Empl[dmWV7$empl == 2] <- 1 dmWV7$Empl[dmWV7$empl == 3] <- 1 dmWV7$Empl[dmWV7$empl == 4] <- 0 dmWV7$Empl[dmWV7$empl == 5] <- 0 dmWV7$Empl[dmWV7$empl == 6] <- 0 dmWV7$Empl[dmWV7$empl == 7] <- 0 dmWV7$Empl[dmWV7$empl == 8] <- 0 table(dmWV7$Empl) ################################################ #################ID############################# ################################################ table(dmAB5$Survey) dmAB5$Survey <- rep("AB", 12061) table(dmWV7$Survey) dmWV7$Survey <- rep("WVS", 6011) dmAB5$id <- c(1:12061) summary(dmAB5$id) length(dmWV7$Survey) dmWV7$id <- c(12062:18072) summary(dmWV7$id) ################################################################################################################## ################################################################################################################## ######################################################2013######################################################## ################################################################################################################## ################################################################################################################## ################################################ #############PREPARING THE DATA################# ################################################ #AB3 AB3 <- read.spss("C:/Uni/Data/ABIII_English.sav",use.value.labels=T, to.data.frame = T) #AB3 <- read.spss("C:/Users/u265173/OneDrive/AB v WVS w Veronica/ABIII_English.sav",use.value.labels=T, to.data.frame = T) table(AB3$country) dmAB3 <- subset(AB3, AB3$country!= "Sudan") dmAB3$country<-droplevels(dmAB3$country) table(dmAB3$country) dmAB3$Surveytype <- 1 table(dmAB3$Surveytype) #WVS6 WV6 <- read.spss("C:/Uni/Data/WV6_full.sav",use.value.labels=T, to.data.frame = T) #WV6 <- read.spss("C:/Users/u265173/OneDrive/AB v WVS w Veronica/WV6_full.sav",use.value.labels=T, to.data.frame = T) table(WV6$V2) dmWV6 <- subset(WV6, WV6$V2=="Algeria" |WV6$V2=="Egypt" | WV6$V2=="Iraq"| WV6$V2=="Jordan"| WV6$V2=="Kuwait"| WV6$V2=="Lebanon"| WV6$V2=="Libya" | WV6$V2=="Morocco" |WV6$V2== "Palestine"| WV6$V2== "Tunisia" | WV6$V2=="Yemen") dmWV6$V2 <- droplevels(dmWV6$V2) table(dmWV6$V2) dmWV6$Surveytype <- 0 table(dmWV6$Surveytype) ################################################################################################################## ################################################################################################################## ##############################################PREPARING ATTITUDES################################################# ################################################################################################################## ################################################################################################################## ############################################################### #################VARIABLE 1: GENERALIZED TRUST################# ############################################################### ######trust AB3 #Generally speaking, would you say that most people can be trusted? #1 = Most people can be trusted #2 = You must be very careful in dealing with people #98 = Cant Choose/Don't know #99 = Decline to Answer summary(dmAB3$q103) dmAB3$trustab <- as.numeric(dmAB3$q103) table(dmAB3$trustab) dmAB3$Trust[dmAB3$trustab == 2] <- 1 dmAB3$Trust[dmAB3$trustab == 3] <- 0 dmAB3$Trust[dmAB3$trustab == 1] <- NA dmAB3$Trust[dmAB3$trustab == 4] <- NA dmAB3$Trust[dmAB3$trustab == 5] <- NA table(dmAB3$Trust) # dmAB3$Trust: 0 - Most people are not trustworthy # 1 - Most people are trustworthy ######trust WV6 #Generally speaking, would you say that most people #can be trusted or that you need to be very careful in dealing with people? #Most people can be trusted #Need to be very careful #No answer -> NA #Don't know -> NA summary(dmWV6$V24) dmWV6$trustwvs <- as.numeric(dmWV6$V24) table(dmWV6$trustwvs) dmWV6$Trust[dmWV6$trustwvs == 1] <- 1 dmWV6$Trust[dmWV6$trustwvs == 2] <- 0 table(dmWV6$Trust) #0 - Need to be very careful # 1 - Most people can be trusted ############################################################### #################VARIABLE 2: EDUCATIONAL GE#################### ############################################################### #AB#: q6014: A university education is more important #for a boy than a girl summary(dmAB3$q6014) dmAB3$unieducab <- as.numeric(dmAB3$q6014) table(dmAB3$unieducab) dmAB3$Unieduc[dmAB3$unieducab == 1] <- NA dmAB3$Unieduc[dmAB3$unieducab == 2] <- 0 dmAB3$Unieduc[dmAB3$unieducab == 3] <- 0.33 dmAB3$Unieduc[dmAB3$unieducab == 4] <- 0.67 dmAB3$Unieduc[dmAB3$unieducab == 5] <- 1 dmAB3$Unieduc[dmAB3$unieducab == 6] <- NA dmAB3$Unieduc[dmAB3$unieducab == 7] <- NA table(dmAB3$Unieduc) # Unieduc: 0 - Strongly agree, 1 - Strongly disagree #WVS6 educge. #V52: A university education is more #important for a boy than for a girl summary(dmWV6$V52) dmWV6$Unieduc <- as.numeric(dmWV6$V52) table(dmWV6$Unieduc) dmWV6$Unieduc[dmWV6$Unieduc == 1] <- 0 dmWV6$Unieduc[dmWV6$Unieduc == 2] <- 0.33 dmWV6$Unieduc[dmWV6$Unieduc == 3] <- 0.67 dmWV6$Unieduc[dmWV6$Unieduc == 4] <- 1 table(dmWV6$Unieduc) ############################################################### #################VARIABLE 3: POLITICAL GE###################### ############################################################### #AB3 #q6013: On the whole, men make better political leaders than women do summary(dmAB3$q6013) dmAB3$polleadab <- as.numeric(dmAB3$q6013) table(dmAB3$polleadab) dmAB3$PolLead[dmAB3$polleadab == 1] <- NA dmAB3$PolLead[dmAB3$polleadab == 2] <- 0 dmAB3$PolLead[dmAB3$polleadab == 3] <- 0.33 dmAB3$PolLead[dmAB3$polleadab == 4] <- 0.67 dmAB3$PolLead[dmAB3$polleadab == 5] <- 1 dmAB3$PolLead[dmAB3$polleadab == 6] <- NA dmAB3$PolLead[dmAB3$polleadab == 7] <- NA table(dmAB3$PolLead) #WVS6 #political GE wvs6. #V51: On the whole, men make better political leaders than women do #V51 summary(dmWV6$V51) dmWV6$poleducwvs <- as.numeric(dmWV6$V51) table(dmWV6$poleduc) dmWV6$PolLead[dmWV6$poleducwvs == 1] <- 0 dmWV6$PolLead[dmWV6$poleducwvs == 2] <- 0.33 dmWV6$PolLead[dmWV6$poleducwvs == 3] <- 0.67 dmWV6$PolLead[dmWV6$poleducwvs == 4] <- 1 table(dmWV6$PolLead) ############################################################### ##############VARIABLE 4: TRUST IN POLICE###################### ############################################################### #AB3 201: 4. #I will name a number of institutions, and I would like you to tell me to #what extent you trust each of them: #public security(the police) summary(dmAB3$q2014) dmAB3$trustpol <-as.numeric(dmAB3$q2014) table(dmAB3$trustpol) dmAB3$Trustpol[dmAB3$trustpol == 1] <- NA dmAB3$Trustpol[dmAB3$trustpol == 2] <- 1 dmAB3$Trustpol[dmAB3$trustpol == 3] <- 0.67 dmAB3$Trustpol[dmAB3$trustpol == 4] <- 0.33 dmAB3$Trustpol[dmAB3$trustpol == 5] <- 0 dmAB3$Trustpol[dmAB3$trustpol == 6] <- NA dmAB3$Trustpol[dmAB3$trustpol == 7] <- NA table(dmAB3$Trustpol) table(dmAB3$Trustpol,dmAB3$q2014) #0 - don't trust; 1 - trust to great extent #WVS6 #ConfidencE: The police summary(dmWV6$V113) dmWV6$trustpol <- as.numeric(dmWV6$V113) table(dmWV6$trustpol) dmWV6$Trustpol[dmWV6$trustpol == 1] <- 1 dmWV6$Trustpol[dmWV6$trustpol == 2] <- 0.67 dmWV6$Trustpol[dmWV6$trustpol == 3] <- 0.33 dmWV6$Trustpol[dmWV6$trustpol == 4] <- 0 table(dmWV6$Trustpol) #0 - no trust at all; 1 - trust to great extent ################################################################################################################## ################################################################################################################## ##############################################PREPARING DEMOGRAPHICS############################################## ################################################################################################################## ################################################################################################################## ################################################ #################COUNTRY######################## ################################################ table(dmAB3$country) dmWV6$country <- as.factor(dmWV6$V2) table (dmAB3$country) table (dmWV6$country) ################################################ #################GENDER######################### ################################################ table(dmAB3$sex) dmAB3$gender[dmAB3$sex == "Male"] <- 0 dmAB3$gender[dmAB3$sex == "Female"] <- 1 table(dmAB3$sex, dmAB3$gender) #0 - male, 1 - female table(dmWV6$V240) dmWV6$gender[dmWV6$V240 == "Male"] <- 0 dmWV6$gender[dmWV6$V240 == "Female"] <- 1 table(dmWV6$V240, dmWV6$gender) ################################################ ###################AGE########################## ################################################ table(dmWV6$V242) summary(dmWV6$V242) #not numeric again. dmWV6$age_short <- as.numeric(paste(dmWV6$V242)) summary(dmWV6$age_short) dmWV6$agecats[dmWV6$age_short < 30] <- 0 dmWV6$agecats[29 < dmWV6$age_short & dmWV6$age_short < 50] <- 1 dmWV6$agecats[dmWV6$age_short > 49] <- 2 table(dmWV6$agecats) table(dmWV6$age_short, dmWV6$agecats) table(dmAB3$q1001) summary(dmAB3$q1001) #also not numeric dmAB3$age_short <- as.numeric(paste(dmAB3$q1001)) summary(dmAB3$age_short) dmAB3$agecats[dmAB3$age_short < 30] <- 0 dmAB3$agecats[29 < dmAB3$age_short & dmAB3$age_short < 50] <- 1 dmAB3$agecats[dmAB3$age_short > 49] <- 2 table(dmAB3$agecats) table(dmAB3$age_short, dmAB3$agecats) #library(rlang) #library(dplyr) ################################################ ###################EDUCATION#################### ################################################ table(dmAB3$q1003) dmAB3$education <- as.numeric(dmAB3$q1003) table(dmAB3$education) dmAB3$edunew <- rep(NA,13609) dmAB3$edunew <- case_when( dmAB3$education == 1 ~ 99, dmAB3$education == 2 ~ 0, dmAB3$education == 3 ~ 1, dmAB3$education == 4 ~ 1, dmAB3$education == 5 ~ 2, dmAB3$education == 6 ~ 2, dmAB3$education == 7 ~ 3, dmAB3$education == 8 ~ 3, dmAB3$education == 9 ~ 99) table(dmAB3$edunew) table(dmAB3$country, dmAB3$edunew) # 0 - No, 1 - Primary, 2 - Secondary, 3 - Tertiary # Tunisia table(dmAB3$q1003t) dmAB3$eductun <- as.numeric(dmAB3$q1003t) table(dmAB3$eductun) dmAB3$edunewtun <- case_when( dmAB3$eductun == 2 ~ 0, dmAB3$eductun == 3 ~ 1, dmAB3$eductun == 4 ~ 1, dmAB3$eductun == 5 ~ 2, dmAB3$eductun == 6 ~ 3) table(dmAB3$edunewtun) table(dmAB3$eductun, dmAB3$q1003t) table(dmAB3$edunewtun, dmAB3$q1003t) #Yemen table(dmAB3$q1003yem) dmAB3$educyem <- as.numeric(dmAB3$q1003yem) table(dmAB3$educyem) dmAB3$edunewyem <- case_when( dmAB3$educyem == 1 ~ 99, dmAB3$educyem == 2 ~ 0, dmAB3$educyem == 3 ~ 1, dmAB3$educyem == 4 ~ 1, dmAB3$educyem == 5 ~ 1, dmAB3$educyem == 6 ~ 2, dmAB3$educyem == 7 ~ 2, dmAB3$educyem == 8 ~ 3, dmAB3$educyem == 9 ~ 3) table(dmAB3$edunewyem) #Adding info on educ in Yemen and Tunisia to edunew variable in dmAB3 table(dmAB3$country, dmAB3$edunew) table(dmAB3$country, dmAB3$edunewyem) table(dmAB3$country, dmAB3$edunewtun) is.factor(dmAB3$edunewtun) dmAB3$edunew <- as.factor(dmAB3$edunew) dmAB3$edunewyem <- as.factor(dmAB3$edunewyem) dmAB3$edunewtun <- as.factor(dmAB3$edunewtun) levels(dmAB3$edunew) levels(dmAB3$edunewyem) levels(dmAB3$edunewtun) table(dmAB3$edunewyem) table(dmAB3$edunewtun) dmAB3$Educ <- dmAB3$edunew table(dmAB3$Educ) dmAB3$Educ[dmAB3$edunewtun == "0"] <- "0" dmAB3$Educ[dmAB3$edunewtun == "1"] <- "1" dmAB3$Educ[dmAB3$edunewtun == "2"] <- "2" dmAB3$Educ[dmAB3$edunewtun == "3"] <- "3" dmAB3$Educ[dmAB3$edunewyem == "0"] <- "0" dmAB3$Educ[dmAB3$edunewyem == "1"] <- "1" dmAB3$Educ[dmAB3$edunewyem == "2"] <- "2" dmAB3$Educ[dmAB3$edunewyem == "3"] <- "3" dmAB3$Educ[dmAB3$edunewyem == "99"] <- "99" table(dmAB3$country, dmAB3$Educ) table(dmAB3$country,dmAB3$edunewyem) table(dmAB3$country,dmAB3$edunewtun) table(dmAB3$Educ) dmAB3$Educ[dmAB3$Educ == 99] <- NA table(dmAB3$Educ) summary(dmAB3$Educ) dmAB3$Educ <- (as.numeric(dmAB3$Educ) - 1) table(dmAB3$Educ) summary(dmAB3$Educ) dmAB3$Educcats[dmAB3$Educ < 3] <- 0 dmAB3$Educcats[dmAB3$Educ == 3] <- 1 table(dmAB3$Educcats, dmAB3$Educ) ###WVS table(dmWV6$V248) #no 0s in NA scores in MENA sample. summary(dmWV6$V248) dmWV6$educ <- as.numeric(dmWV6$V248) table(dmWV6$educ) dmWV6$Educ <- case_when( dmWV6$educ == 1 ~ 0, dmWV6$educ == 2 ~ 0, dmWV6$educ == 3 ~ 1, dmWV6$educ == 4 ~ 1, dmWV6$educ == 5 ~ 2, dmWV6$educ == 6 ~ 1, dmWV6$educ == 7 ~ 2, dmWV6$educ == 8 ~ 2, dmWV6$educ == 9 ~ 3) table(dmWV6$Educ) summary(dmWV6$Educ) dmWV6$Educcats[dmWV6$Educ < 3] <- 0 dmWV6$Educcats[dmWV6$Educ == 3] <- 1 table(dmWV6$Educcats, dmWV6$Educ) ########Not changed #educational categories for descriptives. #dmAB3$Educcats[dmAB3$Education == 0] <- 0 #dmAB3$Educcats[dmAB3$Education == 1] <- 0 #dmAB3$Educcats[dmAB3$Education == 2] <- 0 #dmAB3$Educcats[dmAB3$Education == 3] <- 1 #table(dmAB3$Educcats) #table(dmAB3$Education, dmAB3$Educcats) #educational category with secondary and tertiary to check for tertiary small sample size. #dmAB3$Educcats2[dmAB3$Education == 0] <- 0 #dmAB3$Educcats2[dmAB3$Education == 1] <- 0 #dmAB3$Educcats2[dmAB3$Education == 2] <- 1 #dmAB3$Educcats2[dmAB3$Education == 3] <- 1 #table(dmAB3$Educcats2) #table(dmAB3$Education, dmAB3$Educcats2) ################################################ ###################MARITAL STATUS############### ################################################ table(dmAB3$q1010) dmAB3$marit <- as.numeric(dmAB3$q1010) table(dmAB3$marit) dmAB3$Mar <- case_when( dmAB3$marit == 1 ~ 99, dmAB3$marit == 2 ~ 1, dmAB3$marit == 3 ~ 0, dmAB3$marit == 4 ~ 2, dmAB3$marit == 5 ~ 2, dmAB3$marit == 6 ~ 2, dmAB3$marit == 7 ~ 99) table(dmAB3$Mar) dmAB3$Mar[dmAB3$Mar == 99] <- NA table(dmAB3$Mar) summary(dmAB3$Mar) dmAB3$Mar <- as.factor(dmAB3$Mar) table(dmWV6$V57) dmWV6$marit <- as.numeric(dmWV6$V57) table(dmWV6$marit) dmWV6$Mar <- case_when( dmWV6$marit == 1 ~ 0, dmWV6$marit == 2 ~ 2, dmWV6$marit == 3 ~ 2, dmWV6$marit == 4 ~ 2, dmWV6$marit == 5 ~ 2, dmWV6$marit == 6 ~ 1) table(dmWV6$Mar) summary(dmWV6$Mar) dmWV6$Mar <- as.factor(dmWV6$Mar) # 0 - married, 1 - single, 2 other dmAB3$singled[dmAB3$Mar == 1] <- 1 dmAB3$singled[dmAB3$Mar == 0] <- 0 dmAB3$singled[dmAB3$Mar == 2] <- 0 dmAB3$othermaritald[dmAB3$Mar == 2] <- 1 dmAB3$othermaritald[dmAB3$Mar == 0] <- 0 dmAB3$othermaritald[dmAB3$Mar == 1] <- 0 dmWV6$singled[dmWV6$Mar == 1] <- 1 dmWV6$singled[dmWV6$Mar == 0] <- 0 dmWV6$singled[dmWV6$Mar == 2] <- 0 dmWV6$othermaritald[dmWV6$Mar == 2] <- 1 dmWV6$othermaritald[dmWV6$Mar == 0] <- 0 dmWV6$othermaritald[dmWV6$Mar == 1] <- 0 ################################################ #################EMPLOYMENT STATUS############## ################################################ table(dmAB3$q1004) dmAB3$empl <- as.numeric(dmAB3$q1004) table(dmAB3$empl) dmAB3$Empl <- case_when( dmAB3$empl == 1 ~ 99, dmAB3$empl == 2 ~ 1, dmAB3$empl == 3 ~ 0, dmAB3$empl == 4 ~ 99) table(dmAB3$Empl) dmAB3$Empl[dmAB3$Empl == 99] <- NA table(dmAB3$Empl) table(dmWV6$V229) dmWV6$empl <- as.numeric(dmWV6$V229) table(dmWV6$empl) dmWV6$Empl <- case_when( dmWV6$empl == 1 ~ 1, dmWV6$empl == 2 ~ 1, dmWV6$empl == 3 ~ 1, dmWV6$empl == 4 ~ 0, dmWV6$empl == 5 ~ 0, dmWV6$empl == 6 ~ 0, dmWV6$empl == 7 ~ 0, dmWV6$empl == 8 ~ 0) table(dmWV6$Empl) ################################################ #################ID############################# ################################################ dmAB3$Survey <- rep("AB", 13609) table(dmAB3$Survey) dmWV6$Survey <- rep("WVS", 14162) table(dmWV6$Survey) dmAB3$id <- c(1:13609) summary(dmAB3$id) length(dmWV6$Survey) dmWV6$id <- c(13610:27771) summary(dmWV6$id) ################################################################################################################## ################################################################################################################## ##############################################MERGING 2013 AND 2016############################################### ################################################################################################################## ################################################################################################################## dmAB3$surveyyear <- 1 dmWV6$surveyyear <- 2 dmAB5$surveyyear <- 3 dmWV7$surveyyear <- 4 ABWVS2013 <- merge(dmAB3, dmWV6, by= c("Surveytype", "Empl", "Mar", "singled", "othermaritald", "Educ", "age_short", "agecats", "Educcats", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "surveyyear"), all=TRUE) ABWVS2018 <- merge(dmAB5, dmWV7, by= c("Surveytype", "Empl", "Mar", "singled", "othermaritald", "Educ", "age_short","agecats", "Educcats", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "surveyyear"), all=TRUE) ABWVS20132018 <- merge(ABWVS2013, ABWVS2018, by= c("Surveytype", "Empl", "Mar", "Educ", "singled", "othermaritald", "age_short","agecats", "Educcats", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "surveyyear"), all=TRUE) ABonly <- merge(dmAB3, dmAB5, by= c("Surveytype", "Empl", "Mar", "singled", "othermaritald", "Educ", "age_short", "agecats", "Educcats", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "surveyyear"), all=TRUE) WVSonly <- merge(dmWV7, dmWV6, by= c("Surveytype", "Empl", "Mar", "singled", "othermaritald", "Educ", "age_short", "agecats", "Educcats", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "surveyyear"), all=TRUE) ABWVS20132018$year[ABWVS20132018$surveyyear < 3] <- 2013 ABWVS20132018$year[ABWVS20132018$surveyyear > 2] <- 2018 table(ABWVS20132018$year) table(ABWVS20132018$surveyyear) table(ABWVS20132018$Trustpol, ABWVS20132018$Surveytype) table(ABWVS20132018$Surveytype) ABWVS20132018$Surveytype2[ABWVS20132018$Surveytype == 0] <- 1 ABWVS20132018$Surveytype2[ABWVS20132018$Surveytype == 1] <- 0 table(ABWVS20132018$Surveytype, ABWVS20132018$Surveytype2) table(ABWVS2013$Surveytype) ABWVS2013$Surveytype2[ABWVS2013$Surveytype == 0] <- 1 ABWVS2013$Surveytype2[ABWVS2013$Surveytype == 1] <- 0 table(ABWVS2013$Surveytype, ABWVS2013$Surveytype2) table(ABWVS20132018$Mar) ABWVS20132018$Mar2[ABWVS20132018$Mar == 0] <- 0 ABWVS20132018$Mar2[ABWVS20132018$Mar == 1] <- 1 ABWVS20132018$Mar2[ABWVS20132018$Mar == 2] <- 1 table(ABWVS20132018$Mar, ABWVS20132018$Mar2) table(ABWVS20132018$Trustpol, ABWVS20132018$Surveytype) ################################################################################################################## ################################################################################################################## ###############################################MISSINGNESS PATTERNS############################################### ################################################################################################################## ################################################################################################################## table(ABWVS20132018$year) 27771+18072 #45843 total. table(ABWVS20132018$Trust) 36045+8557 sum(is.na(ABWVS20132018$Trust)) table(ABWVS20132018$Trustpol) 6685 + 8991 + 14660 + 13321 sum(is.na(ABWVS20132018$Trustpol)) table(ABWVS20132018$Unieduc) 5013 + 6335 + 16218 + 17598 sum(is.na(ABWVS20132018$Unieduc)) table(ABWVS20132018$PolLead) 16631 + 14098 + 9620 + 4416 sum(is.na(ABWVS20132018$PolLead)) table(WVSonly$Surveytype) #20173 total sum(is.na(WVSonly$Trust)) sum(is.na(WVSonly$Trustpol)) sum(is.na(WVSonly$Unieduc)) sum(is.na(WVSonly$PolLead)) table(ABonly$Surveytype) #25670 total sum(is.na(ABonly$Trust)) sum(is.na(ABonly$Trustpol)) sum(is.na(ABonly$Unieduc)) sum(is.na(ABonly$PolLead)) table(WVSonly$Trust, WVSonly$country, WVSonly$surveyyear, exclude = NULL) table(WVSonly$Trustpol, WVSonly$country, WVSonly$surveyyear, exclude = NULL) table(WVSonly$Unieduc, WVSonly$country, WVSonly$surveyyear, exclude = NULL) table(WVSonly$PolLead, WVSonly$country, WVSonly$surveyyear, exclude = NULL) table(ABonly$Trust, ABonly$country, ABonly$surveyyear, exclude = NULL) table(ABonly$Trustpol, ABonly$country, ABonly$surveyyear, exclude = NULL) table(ABonly$Unieduc, ABonly$country, ABonly$surveyyear, exclude = NULL) table(ABonly$PolLead, ABonly$country, ABonly$surveyyear, exclude = NULL) table(ABWVS20132018$Trust, ABWVS20132018$country, ABWVS20132018$year, exclude = NULL) table(ABWVS20132018$Trustpol, ABWVS20132018$country, ABWVS20132018$year, exclude = NULL) table(ABWVS20132018$Unieduc, ABWVS20132018$country, ABWVS20132018$year, exclude = NULL) table(ABWVS20132018$PolLead, ABWVS20132018$country, ABWVS20132018$year, exclude = NULL) ################################################################################################################## ################################################################################################################## ###########################################RE-CHECKING TUNISIA 2013 MODELS######################################## ################################################################################################################## ################################################################################################################## Tunisia2013 <- ABWVS2013[ABWVS2013$country == "Tunisia",] table(Tunisia2013$country) table(Tunisia2013$surveyyear) library(lme4) mTunisia20131a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = Tunisia2013, family = binomial) summary(mTunisia20131a) mTunisia20131b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = Tunisia2013, family = binomial) summary(mTunisia20131b) mTunisia20131c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = Tunisia2013, family = binomial) summary(mTunisia20131c) mTunisia20131c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = Tunisia2013, family = binomial) summary(mTunisia20131c2) mTunisia20131d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013, family = binomial) summary(mTunisia20131d) mTunisia20131d2 <- glm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = Tunisia2013, family = binomial) summary(mTunisia20131d2) mTunisia20131e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013, family = binomial) summary(mTunisia20131e) mTunisia20131f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013, family = binomial) summary(mTunisia20131f) table(Tunisia2013$Unieduc) table(Tunisia2013$country) mTunisia20132b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20132b) mTunisia20132b2 <- lm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20132b2) mTunisia20132c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20132c) mTunisia20132c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20132c2) mTunisia20132d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20132d) mTunisia20132e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20132e) mTunisia20132f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20132f) table(Tunisia2013$PolLead) table(Tunisia2013$country) mTunisia20133b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20133b) mTunisia20133c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20133c) mTunisia20133c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20133c2) mTunisia20133d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20133d) mTunisia20133d2 <- lm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20133d2) mTunisia20133e <- lm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia2013) summary(mTunisia20133e) mTunisia20133f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20133f) mTunisia20133f2 <- lm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20133f2) table(Tunisia2013$Trustpol) table(Tunisia2013$country) mTunisia20134b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20134b2) mTunisia20134b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20134b) mTunisia20134c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20134c) mTunisia20134d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20134d) mTunisia20134d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20134d2) mTunisia20134e <- lm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia2013) summary(mTunisia20134e) mTunisia20134f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20134f) mTunisia20134f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = Tunisia2013) summary(mTunisia20134f2) ################################################################################################################## ################################################################################################################## ################################################WEIGHTED ANALYSES################################################# ################################################################################################################## ################################################################################################################## ################################################################################################################## ################################################################################################################## ######################################CREATING WEIGHTS ALGERIA 2013############################################### ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Algeria2013 <- ABWVS20132018[ABWVS20132018$country == "Algeria" & ABWVS20132018$year == 2013,] table(Algeria2013$country) table(Algeria2013$year) Algeria2013$weightcats <- 99999 table(Algeria2013$weightcats) Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 1 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 2 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 3 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 4 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 5 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 6 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 7 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 8 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 9 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 10 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 11 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 12 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 13 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 14 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 15 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 16 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 17 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 18 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 19 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 20 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 21 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 22 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 23 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 0 ] <- 24 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 25 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 26 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 27 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 28 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 29 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 30 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 31 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 32 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 33 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 34 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 35 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 36 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 37 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 38 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 39 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 40 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 41 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 42 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 43 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 44 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 45 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 46 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 47 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 0 ] <- 48 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 49 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 50 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 51 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 52 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 53 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 54 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 55 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 56 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 57 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 58 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 59 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 60 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 61 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 62 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 63 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 64 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 65 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 66 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 67 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 68 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 69 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 70 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 71 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==0 & Algeria2013$Surveytype == 1 ] <- 72 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 73 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 74 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 75 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 76 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 77 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 78 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 79 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 80 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 81 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 82 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 83 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==0 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 84 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 85 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 86 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 87 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 88 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 89 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==0 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 90 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 91 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==0 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 92 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 93 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==1 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 94 Algeria2013$weightcats[Algeria2013$gender == 0 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 95 Algeria2013$weightcats[Algeria2013$gender == 1 & Algeria2013$agecats ==2 & Algeria2013$Educcats ==1 & Algeria2013$Mar2 ==1 & Algeria2013$Empl ==1 & Algeria2013$Surveytype == 1 ] <- 96 table(Algeria2013$weightcats) Algeria2013nomiss <- Algeria2013[Algeria2013$weightcats != 99999,] table(Algeria2013nomiss$weightcats) prop.table(table(Algeria2013nomiss$weightcats, Algeria2013nomiss$Surveytype)) #cats without respondents: 9 23 24 31 47 48 55 57 59 60 71 72 74 79 84 89 95 96 Algeria2013nomiss$weightcats[Algeria2013nomiss$weightcats == 9 & Algeria2013nomiss$weightcats == 23 & Algeria2013nomiss$weightcats == 24 & Algeria2013nomiss$weightcats == 31 & Algeria2013nomiss$weightcats == 47 & Algeria2013nomiss$weightcats == 48 & Algeria2013nomiss$weightcats == 55 & Algeria2013nomiss$weightcats == 57 & Algeria2013nomiss$weightcats == 59 & Algeria2013nomiss$weightcats == 60 & Algeria2013nomiss$weightcats == 71 & Algeria2013nomiss$weightcats == 72 & Algeria2013nomiss$weightcats == 74 & Algeria2013nomiss$weightcats == 79 & Algeria2013nomiss$weightcats == 84 & Algeria2013nomiss$weightcats == 89 & Algeria2013nomiss$weightcats == 95 & Algeria2013nomiss$weightcats == 96] <- NA table(Algeria2013nomiss$weightcats) Algeria13.distribution <- data.frame(weightcats = c("1", "2", "3", "4", "5", "6", "7", "8", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "25", "26", "27", "28", "29", "30", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "49", "50", "51", "52", "53", "54", "56", "58", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "73", "75", "76", "77", "78", "80", "81", "82", "83", "85", "86", "87", "88", "90", "91", "92", "93", "94"), Freq = nrow(Algeria2013nomiss) * c( 0.0008267879, 0.0070276974, 0.0057875155, 0.0442331542, 0.0198429103, 0.0322447292, 0.000413394, 0.0008267879, 0.0008267879, 0.000413394, 0.000413394, 0.046713518, 0.0661430343, 0.0086812733, 0.0186027284, 0.0103348491, 0.011988425, 0.0074410914, 0.0086812733, 0.0020669698, 0.0024803638, 0.0028937578, 0.0041339396, 0.0595287309, 0.0140553948, 0.0194295163, 0.0033071517, 0.0008267879, 0.0082678793, 0.0045473336, 0.0020669698, 0.000413394, 0.0202563043, 0.0099214551, 0.0252170318, 0.0099214551, 0.0049607276, 0.0012401819, 0.0024803638, 0.000413394, 0.0033071517, 0.0024803638, 0.0008267879, 0.0070276974, 0.0057875155, 0.0442331542, 0.0198429103, 0.0322447292, 0.0008267879, 0.0008267879, 0.046713518, 0.0661430343, 0.0086812733, 0.0186027284, 0.0103348491, 0.011988425, 0.0074410914, 0.0086812733, 0.0020669698, 0.0024803638, 0.0028937578, 0.0595287309, 0.0140553948, 0.0194295163, 0.0033071517, 0.0008267879, 0.0082678793, 0.0045473336, 0.0020669698, 0.0202563043, 0.0099214551, 0.0252170318, 0.0099214551, 0.0012401819, 0.0024803638, 0.000413394, 0.0033071517, 0.0024803638)) library(survey) Algeria2013w <- svydesign(ids=~1, data = Algeria2013nomiss) Algeria2013weighted <- rake(design = Algeria2013w, sample.margins = list(~weightcats), population.margins = list(Algeria13.distribution)) table(Algeria2013nomiss$Surveytype) svytable(~Surveytype, Algeria2013weighted) #WORKED. table(Algeria2013nomiss$gender) svytable(~gender, Algeria2013weighted) #worked. table(Algeria2013nomiss$gender, Algeria2013nomiss$Surveytype) svytable(~interaction(gender, Surveytype), design = Algeria2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## ########################################ANALYSES ALGERIA 2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(table(Algeria2013nomiss$Unieduc, Algeria2013nomiss$Surveytype), 2) prop.table(svytable(~Trust, subset(Algeria2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Algeria2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Algeria2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Algeria2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Algeria2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Algeria2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Algeria2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Algeria2013weighted, Surveytype ==1))) #TRUST mAlgeria2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted, family = quasibinomial) summary(mAlgeria2013weighted21b) mAlgeria2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted, family = quasibinomial) summary(mAlgeria2013weighted21c) mAlgeria2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted, family = quasibinomial) summary(mAlgeria2013weighted21c2) mAlgeria2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted, family = quasibinomial) summary(mAlgeria2013weighted21d) mAlgeria2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted, family = quasibinomial) summary(mAlgeria2013weighted21d2) mAlgeria2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted, family = quasibinomial) summary(mAlgeria2013weighted21e) mAlgeria2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted, family = quasibinomial) summary(mAlgeria2013weighted21e2) mAlgeria2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted, family = quasibinomial) summary(mAlgeria2013weighted21f) mAlgeria2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted, family = quasibinomial) summary(mAlgeria2013weighted21f2) #TRUST POL mAlgeria20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Algeria2013nomiss) summary(mAlgeria20132b) mAlgeria2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted24c) mAlgeria2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted24d) mAlgeria2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted24d2) mAlgeria2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted24e) mAlgeria2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted24f) mAlgeria2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted24f2) #EDUC GE mAlgeria2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted22b) mAlgeria2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted22b2) mAlgeria2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted22c) mAlgeria2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted22d) mAlgeria2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted22e) mAlgeria2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted22f) mAlgeria2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted22f2) #POL GE mAlgeria20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Algeria2013nomiss) summary(mAlgeria20132b) mAlgeria2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted23c) mAlgeria2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted23d) mAlgeria2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted23e) mAlgeria2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Algeria2013weighted) summary(mAlgeria2013weighted23f) ################################################################################################################## ################################################################################################################## ########################################CREATING WEIGHTS EGYPT 2013############################################### ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Egypt2013 <- ABWVS20132018[ABWVS20132018$country == "Egypt" & ABWVS20132018$year == 2013,] table(Egypt2013$country) table(Egypt2013$year) Egypt2013$weightcats <- 99999 table(Egypt2013$weightcats) Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 1 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 2 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 3 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 4 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 5 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 6 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 7 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 8 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 9 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 10 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 11 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 12 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 13 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 14 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 15 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 16 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 17 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 18 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 19 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 20 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 21 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 22 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 23 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 0 ] <- 24 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 25 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 26 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 27 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 28 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 29 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 30 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 31 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 32 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 33 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 34 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 35 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 36 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 37 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 38 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 39 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 40 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 41 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 42 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 43 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 44 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 45 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 46 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 47 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 0 ] <- 48 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 49 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 50 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 51 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 52 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 53 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 54 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 55 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 56 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 57 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 58 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 59 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 60 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 61 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 62 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 63 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 64 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 65 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 66 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 67 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 68 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 69 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 70 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 71 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==0 & Egypt2013$Surveytype == 1 ] <- 72 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 73 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 74 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 75 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 76 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 77 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 78 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 79 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 80 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 81 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 82 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 83 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==0 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 84 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 85 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 86 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 87 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 88 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 89 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==0 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 90 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 91 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==0 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 92 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 93 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==1 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 94 Egypt2013$weightcats[Egypt2013$gender == 0 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 95 Egypt2013$weightcats[Egypt2013$gender == 1 & Egypt2013$agecats ==2 & Egypt2013$Educcats ==1 & Egypt2013$Mar2 ==1 & Egypt2013$Empl ==1 & Egypt2013$Surveytype == 1 ] <- 96 table(Egypt2013$weightcats) Egypt2013nomiss <- Egypt2013[Egypt2013$weightcats != 99999,] table(Egypt2013nomiss$weightcats) prop.table(table(Egypt2013nomiss$weightcats, Egypt2013nomiss$Surveytype)) #cats without respondents at all: 7 9 21 23 47 55 57 71 84 #cats without respondents in their accompanying WVS/AB countercategory: 69 36 95 Egypt2013nomiss2 <- Egypt2013nomiss[Egypt2013nomiss$weightcats != 69 & Egypt2013nomiss$weightcats != 36 & Egypt2013nomiss$weightcats != 95 ,] table(Egypt2013nomiss2$weightcats) #Egypt2013nomiss$weightcats[Egypt2013nomiss$weightcats == 7 & Egypt2013nomiss$weightcats == 9 & Egypt2013nomiss$weightcats == 21 & Egypt2013nomiss$weightcats == 23 & Egypt2013nomiss$weightcats == 47 & Egypt2013nomiss$weightcats == 55 & Egypt2013nomiss$weightcats == 57 & Egypt2013nomiss$weightcats == 71 & Egypt2013nomiss$weightcats == 84 ] <- NA #table(Egypt2013nomiss$weightcats) #Egypt2013nomiss$weightcats[Egypt2013nomiss$weightcats == 36 ] <- NA #Egypt2013nomiss$weightcats[Egypt2013nomiss$weightcats == 69 ] <- NA #Egypt2013nomiss$weightcats[Egypt2013nomiss$weightcats == 95 ] <- NA #table(Egypt2013nomiss$weightcats) Egypt13.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "8", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "22", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "48", "49", "50", "51", "52", "53", "54", "56", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "70", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "96"), Freq = nrow(Egypt2013nomiss2) * c( 0.0007355645, 0.0639941155, 0.0033100405, 0.1187936741, 0.0158146377, 0.0415593968, 0.0055167341, 0.0091945568, 0.0022066936, 0.0025744759, 0.012136815, 0.0246414123, 0.0007355645, 0.0154468555, 0.0044133873, 0.0467083487, 0.004045605, 0.0055167341, 0.0014711291, 0.0018389114, 0.00809121, 0.0018389114, 0.044501655, 0.0128723796, 0.0275836705, 0.0033100405, 0.0018389114, 0.0018389114, 0.0136079441, 0.00809121, 0.0106656859, 0.0136079441, 0.0025744759, 0.0077234277, 0.0018389114, 0.0025744759, 0.0022066936, 0.0051489518, 0.0036778227, 0.0014711291, 0.0014711291, 0.0003677823, 0.0007355645, 0.0639941155, 0.0033100405, 0.1187936741, 0.0158146377, 0.0415593968, 0.0055167341, 0.0091945568, 0.0022066936, 0.0025744759, 0.012136815, 0.0246414123, 0.0007355645, 0.0154468555, 0.0044133873, 0.0467083487, 0.004045605, 0.0055167341, 0.0014711291, 0.0018389114, 0.00809121, 0.0018389114, 0.044501655, 0.0128723796, 0.0275836705, 0.0033100405, 0.0018389114, 0.0018389114, 0.0136079441, 0.00809121, 0.0106656859, 0.0136079441, 0.0025744759, 0.0077234277, 0.0018389114, 0.0025744759, 0.0022066936, 0.0051489518, 0.0036778227, 0.0014711291, 0.0014711291, 0.0003677823)) library(survey) Egypt2013w <- svydesign(ids=~1, data = Egypt2013nomiss2) Egypt2013weighted <- rake(design = Egypt2013w, sample.margins = list(~weightcats), population.margins = list(Egypt13.distribution)) table(Egypt2013nomiss$Surveytype) svytable(~Surveytype, Egypt2013weighted) 1523+1196 #WORKED. table(Egypt2013nomiss$gender) svytable(~gender, Egypt2013weighted) #worked. table(Egypt2013nomiss$gender, Egypt2013nomiss$Surveytype) svytable(~interaction(gender, Surveytype), design = Egypt2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## ###########################################ANALYSES EGYPT2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Egypt2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Egypt2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Egypt2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Egypt2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Egypt2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Egypt2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Egypt2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Egypt2013weighted, Surveytype ==1))) #TRUST mEgypt2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted, family = quasibinomial) summary(mEgypt2013weighted21b) mEgypt2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted, family = quasibinomial) summary(mEgypt2013weighted21c) mEgypt2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted, family = quasibinomial) summary(mEgypt2013weighted21c2) mEgypt2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted, family = quasibinomial) summary(mEgypt2013weighted21d) mEgypt2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted, family = quasibinomial) summary(mEgypt2013weighted21d2) mEgypt2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted, family = quasibinomial) summary(mEgypt2013weighted21e) mEgypt2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted, family = quasibinomial) summary(mEgypt2013weighted21e2) mEgypt2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted, family = quasibinomial) summary(mEgypt2013weighted21f) mEgypt2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted, family = quasibinomial) summary(mEgypt2013weighted21f2) #TRUST POL mEgypt20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt2013nomiss) summary(mEgypt20132b) mEgypt2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted24c) mEgypt2013weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted24c2) mEgypt2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted24d) mEgypt2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted24d2) mEgypt2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted24e) mEgypt2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted24f) mEgypt2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted24f2) #EDUC GE mEgypt2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted22b) mEgypt2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted22b2) mEgypt2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted22c) mEgypt2013weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted22c2) mEgypt2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted22d) mEgypt2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted22e) mEgypt2013weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted22e2) mEgypt2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted22f) mEgypt2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted22f2) #POL GE mEgypt20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt2013nomiss) summary(mEgypt20132b) mEgypt2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted23c) mEgypt2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted23d) mEgypt2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted23e) mEgypt2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2013weighted) summary(mEgypt2013weighted23f) ################################################################################################################## ################################################################################################################## ########################################CREATING WEIGHTS IRAQ 2013############################################### ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Iraq2013 <- ABWVS20132018[ABWVS20132018$country == "Iraq" & ABWVS20132018$year == 2013,] table(Iraq2013$country) table(Iraq2013$year) Iraq2013$weightcats <- 99999 table(Iraq2013$weightcats) Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 1 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 2 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 3 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 4 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 5 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 6 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 7 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 8 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 9 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 10 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 11 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 12 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 13 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 14 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 15 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 16 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 17 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 18 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 19 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 20 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 21 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 22 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 23 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 0 ] <- 24 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 25 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 26 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 27 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 28 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 29 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 30 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 31 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 32 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 33 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 34 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 35 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 36 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 37 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 38 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 39 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 40 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 41 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 42 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 43 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 44 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 45 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 46 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 47 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 0 ] <- 48 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 49 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 50 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 51 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 52 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 53 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 54 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 55 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 56 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 57 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 58 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 59 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 60 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 61 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 62 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 63 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 64 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 65 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 66 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 67 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 68 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 69 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 70 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 71 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==0 & Iraq2013$Surveytype == 1 ] <- 72 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 73 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 74 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 75 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 76 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 77 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 78 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 79 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 80 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 81 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 82 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 83 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==0 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 84 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 85 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 86 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 87 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 88 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 89 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==0 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 90 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 91 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==0 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 92 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 93 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==1 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 94 Iraq2013$weightcats[Iraq2013$gender == 0 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 95 Iraq2013$weightcats[Iraq2013$gender == 1 & Iraq2013$agecats ==2 & Iraq2013$Educcats ==1 & Iraq2013$Mar2 ==1 & Iraq2013$Empl ==1 & Iraq2013$Surveytype == 1 ] <- 96 table(Iraq2013$weightcats) Iraq2013nomiss <- Iraq2013[Iraq2013$weightcats != 99999,] table(Iraq2013nomiss$weightcats) prop.table(table(Iraq2013nomiss$weightcats, Iraq2013nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 24 71 48 Iraq2013nomiss2 <- Iraq2013nomiss[Iraq2013nomiss$weightcats != 24 & Iraq2013nomiss$weightcats != 71 & Iraq2013nomiss$weightcats != 48 ,] table(Iraq2013nomiss2$weightcats) Iraq13.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "22", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "49", "50", "51", "52", "53", "54", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "70", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94"), Freq = nrow(Iraq2013nomiss2) * c( 0.0074595939, 0.0273518442, 0.0165768753, 0.0799834231, 0.021135516, 0.0285951098, 0.0012432656, 0.0012432656, 0.0033153751, 0.0029009532, 0.0004144219, 0.0406133444, 0.0381268131, 0.0008288438, 0.0116038127, 0.0020721094, 0.0099461252, 0.0049730626, 0.0016576875, 0.0004144219, 0.0194778284, 0.0033153751, 0.0650642354, 0.0091172814, 0.0190634065, 0.0004144219, 0.0033153751, 0.0016576875, 0.0145047659, 0.0095317033, 0.0062163282, 0.0008288438, 0.024450891, 0.0016576875, 0.0033153751, 0.0020721094, 0.0004144219, 0.0004144219, 0.0049730626, 0.0016576875, 0.0020721094, 0.0016576875, 0.0074595939, 0.0273518442, 0.0165768753, 0.0799834231, 0.021135516, 0.0285951098, 0.0012432656, 0.0012432656, 0.0033153751, 0.0029009532, 0.0004144219, 0.0406133444, 0.0381268131, 0.0008288438, 0.0116038127, 0.0020721094, 0.0099461252, 0.0049730626, 0.0016576875, 0.0004144219, 0.0194778284, 0.0033153751, 0.0650642354, 0.0091172814, 0.0190634065, 0.0004144219, 0.0033153751, 0.0016576875, 0.0145047659, 0.0095317033, 0.0062163282, 0.0008288438, 0.024450891, 0.0016576875, 0.0033153751, 0.0020721094, 0.0004144219, 0.0004144219, 0.0049730626, 0.0016576875, 0.0020721094, 0.0016576875)) library(survey) Iraq2013w <- svydesign(ids=~1, data = Iraq2013nomiss2) Iraq2013weighted <- rake(design = Iraq2013w, sample.margins = list(~weightcats), population.margins = list(Iraq13.distribution)) table(Iraq2013nomiss2$Surveytype) svytable(~Surveytype, Iraq2013weighted) #WORKED. table(Iraq2013nomiss2$gender) svytable(~gender, Iraq2013weighted) #worked. table(Iraq2013nomiss$gender, Iraq2013nomiss$Surveytype) svytable(~interaction(gender, Surveytype), design = Iraq2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## ###########################################ANALYSES IRAQ2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Iraq2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Iraq2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Iraq2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Iraq2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Iraq2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Iraq2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Iraq2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Iraq2013weighted, Surveytype ==1))) #TRUST mIraq2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted, family = quasibinomial) summary(mIraq2013weighted21b) mIraq2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted, family = quasibinomial) summary(mIraq2013weighted21c) mIraq2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted, family = quasibinomial) summary(mIraq2013weighted21c2) mIraq2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted, family = quasibinomial) summary(mIraq2013weighted21d) mIraq2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted, family = quasibinomial) summary(mIraq2013weighted21d2) mIraq2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted, family = quasibinomial) summary(mIraq2013weighted21e) mIraq2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted, family = quasibinomial) summary(mIraq2013weighted21e2) mIraq2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted, family = quasibinomial) summary(mIraq2013weighted21f) mIraq2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted, family = quasibinomial) summary(mIraq2013weighted21f2) #TRUST POL mIraq20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq2013nomiss) summary(mIraq20132b) mIraq20132b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq2013nomiss) summary(mIraq20132b2) mIraq2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted24c) mIraq2013weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted24c2) mIraq2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted24d) mIraq2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted24d2) mIraq2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted24e) mIraq2013weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted24e2) mIraq2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted24f) mIraq2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted24f2) #EDUC GE mIraq2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted22b) mIraq2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted22b2) mIraq2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted22c) mIraq2013weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted22c2) mIraq2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted22d) mIraq2013weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted22d2) mIraq2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted22e) mIraq2013weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted22e2) mIraq2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted22f) mIraq2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted22f2) #POL GE mIraq20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq2013nomiss) summary(mIraq20132b) mIraq2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted23c) mIraq2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted23d) mIraq2013weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted23d2) mIraq2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted23e) mIraq2013weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted23e2) mIraq2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2013weighted) summary(mIraq2013weighted23f) ################################################################################################################## ################################################################################################################## ########################################CREATING WEIGHTS JORDAN 2013############################################### ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Jordan2013 <- ABWVS20132018[ABWVS20132018$country == "Jordan" & ABWVS20132018$year == 2013,] table(Jordan2013$country) table(Jordan2013$year) Jordan2013$weightcats <- 99999 table(Jordan2013$weightcats) Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 1 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 2 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 3 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 4 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 5 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 6 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 7 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 8 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 9 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 10 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 11 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 12 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 13 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 14 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 15 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 16 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 17 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 18 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 19 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 20 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 21 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 22 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 23 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 0 ] <- 24 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 25 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 26 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 27 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 28 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 29 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 30 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 31 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 32 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 33 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 34 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 35 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 36 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 37 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 38 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 39 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 40 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 41 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 42 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 43 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 44 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 45 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 46 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 47 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 0 ] <- 48 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 49 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 50 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 51 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 52 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 53 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 54 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 55 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 56 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 57 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 58 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 59 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 60 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 61 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 62 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 63 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 64 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 65 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 66 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 67 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 68 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 69 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 70 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 71 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==0 & Jordan2013$Surveytype == 1 ] <- 72 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 73 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 74 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 75 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 76 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 77 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 78 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 79 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 80 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 81 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 82 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 83 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==0 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 84 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 85 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 86 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 87 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 88 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 89 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==0 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 90 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 91 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==0 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 92 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 93 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==1 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 94 Jordan2013$weightcats[Jordan2013$gender == 0 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 95 Jordan2013$weightcats[Jordan2013$gender == 1 & Jordan2013$agecats ==2 & Jordan2013$Educcats ==1 & Jordan2013$Mar2 ==1 & Jordan2013$Empl ==1 & Jordan2013$Surveytype == 1 ] <- 96 table(Jordan2013$weightcats) Jordan2013nomiss <- Jordan2013[Jordan2013$weightcats != 99999,] table(Jordan2013nomiss$weightcats) prop.table(table(Jordan2013nomiss$weightcats, Jordan2013nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 57 71 90 95 Jordan2013nomiss2 <- Jordan2013nomiss[Jordan2013nomiss$weightcats != 57 & Jordan2013nomiss$weightcats != 71 & Jordan2013nomiss$weightcats != 90 & Jordan2013nomiss$weightcats != 95 ,] table(Jordan2013nomiss2$weightcats) Jordan13.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "8", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "43", "44", "45", "46", "49", "50", "51", "52", "53", "54", "56", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "91", "92", "93", "94"), Freq = nrow(Jordan2013nomiss2) * c( 0.0016700067, 0.0250501002, 0.0146960588, 0.080494322, 0.0313961256, 0.0297261189, 0.0036740147, 0.00501002, 0.006012024, 0.001002004, 0.0307281229, 0.0126920508, 0.0013360053, 0.0103540414, 0.002004008, 0.0126920508, 0.0033400134, 0.0026720107, 0.0006680027, 0.0003340013, 0.0003340013, 0.0070140281, 0.0003340013, 0.044756179, 0.0043420174, 0.0136940548, 0.0003340013, 0.001002004, 0.001002004, 0.0080160321, 0.00501002, 0.0023380094, 0.0003340013, 0.0197060788, 0.0006680027, 0.0043420174, 0.0033400134, 0.0003340013, 0.006012024, 0.0006680027, 0.0013360053, 0.0003340013, 0.0016700067, 0.0250501002, 0.0146960588, 0.080494322, 0.0313961256, 0.0297261189, 0.0036740147, 0.00501002, 0.006012024, 0.001002004, 0.0307281229, 0.0126920508, 0.0013360053, 0.0103540414, 0.002004008, 0.0126920508, 0.0033400134, 0.0026720107, 0.0006680027, 0.0003340013, 0.0003340013, 0.0070140281, 0.0003340013, 0.044756179, 0.0043420174, 0.0136940548, 0.0003340013, 0.001002004, 0.001002004, 0.0080160321, 0.00501002, 0.0023380094, 0.0003340013, 0.0197060788, 0.0006680027, 0.0043420174, 0.0033400134, 0.0003340013, 0.006012024, 0.0006680027, 0.0013360053, 0.0003340013)) #library(survey) Jordan2013w <- svydesign(ids=~1, data = Jordan2013nomiss2) Jordan2013weighted <- rake(design = Jordan2013w, sample.margins = list(~weightcats), population.margins = list(Jordan13.distribution)) table(Jordan2013nomiss2$Surveytype) svytable(~Surveytype, Jordan2013weighted) #WORKED. table(Jordan2013nomiss2$gender) svytable(~gender, Jordan2013weighted) #worked. table(Jordan2013nomiss2$gender, Jordan2013nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Jordan2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## ###########################################ANALYSES JORDAN2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Jordan2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Jordan2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Jordan2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Jordan2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Jordan2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Jordan2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Jordan2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Jordan2013weighted, Surveytype ==1))) #TRUST mJordan2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted, family = quasibinomial) summary(mJordan2013weighted21b) mJordan2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted, family = quasibinomial) summary(mJordan2013weighted21c) mJordan2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted, family = quasibinomial) summary(mJordan2013weighted21c2) mJordan2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted, family = quasibinomial) summary(mJordan2013weighted21d) mJordan2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted, family = quasibinomial) summary(mJordan2013weighted21d2) mJordan2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted, family = quasibinomial) summary(mJordan2013weighted21e) mJordan2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted, family = quasibinomial) summary(mJordan2013weighted21e2) mJordan2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted, family = quasibinomial) summary(mJordan2013weighted21f) mJordan2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted, family = quasibinomial) summary(mJordan2013weighted21f2) #TRUST POL mJordan20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan2013nomiss) summary(mJordan20132b) mJordan20132b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Jordan2013nomiss) summary(mJordan20132b2) mJordan2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted24c) mJordan2013weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted24c2) mJordan2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted24d) mJordan2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted24d2) mJordan2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted24e) mJordan2013weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted24e2) mJordan2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted24f) mJordan2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted24f2) #EDUC GE mJordan2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted22b) mJordan2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted22b2) mJordan2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted22c) mJordan2013weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted22c2) mJordan2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted22d) mJordan2013weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted22d2) mJordan2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted22e) mJordan2013weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted22e2) mJordan2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted22f) mJordan2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted22f2) #POL GE mJordan20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan2013nomiss) summary(mJordan20132b) mJordan20132b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Jordan2013nomiss) summary(mJordan20132b2) mJordan2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted23c) mJordan2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted23d) mJordan2013weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted23d2) mJordan2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted23e) mJordan2013weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted23e2) mJordan2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted23f) mJordan2013weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2013weighted) summary(mJordan2013weighted23f2) ################################################################################################################## ################################################################################################################## ########################################CREATING WEIGHTS KUWAIT 2013############################################### ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Kuwait2013 <- ABWVS20132018[ABWVS20132018$country == "Kuwait" & ABWVS20132018$year == 2013,] table(Kuwait2013$country) table(Kuwait2013$year) Kuwait2013$weightcats <- 99999 table(Kuwait2013$weightcats) Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 1 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 2 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 3 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 4 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 5 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 6 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 7 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 8 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 9 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 10 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 11 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 12 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 13 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 14 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 15 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 16 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 17 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 18 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 19 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 20 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 21 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 22 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 23 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 0 ] <- 24 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 25 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 26 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 27 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 28 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 29 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 30 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 31 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 32 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 33 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 34 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 35 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 36 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 37 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 38 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 39 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 40 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 41 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 42 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 43 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 44 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 45 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 46 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 47 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 0 ] <- 48 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 49 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 50 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 51 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 52 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 53 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 54 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 55 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 56 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 57 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 58 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 59 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 60 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 61 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 62 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 63 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 64 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 65 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 66 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 67 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 68 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 69 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 70 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 71 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==0 & Kuwait2013$Surveytype == 1 ] <- 72 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 73 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 74 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 75 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 76 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 77 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 78 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 79 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 80 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 81 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 82 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 83 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==0 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 84 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 85 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 86 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 87 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 88 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 89 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==0 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 90 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 91 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==0 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 92 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 93 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==1 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 94 Kuwait2013$weightcats[Kuwait2013$gender == 0 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 95 Kuwait2013$weightcats[Kuwait2013$gender == 1 & Kuwait2013$agecats ==2 & Kuwait2013$Educcats ==1 & Kuwait2013$Mar2 ==1 & Kuwait2013$Empl ==1 & Kuwait2013$Surveytype == 1 ] <- 96 table(Kuwait2013$weightcats) table(Kuwait2013$weightcats, Kuwait2013$Surveytype) #100 missings in wvs. Kuwait2013nomiss <- Kuwait2013[Kuwait2013$weightcats != 99999,] table(Kuwait2013nomiss$weightcats) prop.table(table(Kuwait2013nomiss$weightcats, Kuwait2013nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 7 8 22 23 24 84 47 48 Kuwait2013nomiss2 <- Kuwait2013nomiss[Kuwait2013nomiss$weightcats != 7 & Kuwait2013nomiss$weightcats != 8 & Kuwait2013nomiss$weightcats != 22 & Kuwait2013nomiss$weightcats != 23 & Kuwait2013nomiss$weightcats != 24 & Kuwait2013nomiss$weightcats != 84 & Kuwait2013nomiss$weightcats != 47 & Kuwait2013nomiss$weightcats != 48 ,] table(Kuwait2013nomiss2$weightcats) Kuwait13.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "9", "10", "11", "13", "14", "15", "16", "17", "18", "19", "20", "21", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "37", "38", "39", "40", "41", "43", "44", "45", "46", "49", "50", "51", "52", "53", "54", "57", "58", "59", "61", "62", "63", "64", "65", "66", "67", "68", "69", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "85", "86", "87", "88", "89", "91", "92", "93", "94"), Freq = nrow(Kuwait2013nomiss2) * c( 0.00270027, 0.005850585, 0.00270027, 0.01260126, 0.025652565, 0.00630063, 0.00090009, 0.002250225, 0.00270027, 0.03420342, 0.004950495, 0.002250225, 0.013051305, 0.000450045, 0.00810081, 0.00270027, 0.002250225, 0.00180018, 0.02160216, 0.011251125, 0.086408641, 0.03690369, 0.012151215, 0.003150315, 0.013051305, 0.010351035, 0.04410441, 0.029252925, 0.006750675, 0.04140414, 0.013051305, 0.031053105, 0.02610261, 0.00270027, 0.00270027, 0.005850585, 0.003150315, 0.00360036, 0.00270027, 0.005850585, 0.00270027, 0.01260126, 0.025652565, 0.00630063, 0.00090009, 0.002250225, 0.00270027, 0.03420342, 0.004950495, 0.002250225, 0.013051305, 0.000450045, 0.00810081, 0.00270027, 0.002250225, 0.00180018, 0.02160216, 0.011251125, 0.086408641, 0.03690369, 0.012151215, 0.003150315, 0.013051305, 0.010351035, 0.04410441, 0.029252925, 0.006750675, 0.04140414, 0.013051305, 0.031053105, 0.02610261, 0.00270027, 0.00270027, 0.005850585, 0.003150315, 0.00360036)) #library(survey) Kuwait2013w <- svydesign(ids=~1, data = Kuwait2013nomiss2) Kuwait2013weighted <- rake(design = Kuwait2013w, sample.margins = list(~weightcats), population.margins = list(Kuwait13.distribution)) table(Kuwait2013nomiss2$Surveytype) svytable(~Surveytype, Kuwait2013weighted) #WORKED. table(Kuwait2013nomiss2$gender) svytable(~gender, Kuwait2013weighted) #worked. table(Kuwait2013nomiss2$gender, Kuwait2013nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Kuwait2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## ###########################################ANALYSES KUWAIT2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Kuwait2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Kuwait2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Kuwait2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Kuwait2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Kuwait2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Kuwait2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Kuwait2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Kuwait2013weighted, Surveytype ==1))) #TRUST mKuwait2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted, family = quasibinomial) summary(mKuwait2013weighted21b) mKuwait2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted, family = quasibinomial) summary(mKuwait2013weighted21c) mKuwait2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted, family = quasibinomial) summary(mKuwait2013weighted21c2) mKuwait2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted, family = quasibinomial) summary(mKuwait2013weighted21d) mKuwait2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted, family = quasibinomial) summary(mKuwait2013weighted21d2) mKuwait2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted, family = quasibinomial) summary(mKuwait2013weighted21e) mKuwait2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted, family = quasibinomial) summary(mKuwait2013weighted21e2) mKuwait2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted, family = quasibinomial) summary(mKuwait2013weighted21f) mKuwait2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted, family = quasibinomial) summary(mKuwait2013weighted21f2) #TRUST POL mKuwait20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Kuwait2013nomiss) summary(mKuwait20132b) mKuwait20132b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Kuwait2013nomiss) summary(mKuwait20132b2) mKuwait2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted24c) mKuwait2013weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted24c2) mKuwait2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted24d) mKuwait2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted24d2) mKuwait2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted24e) mKuwait2013weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted24e2) mKuwait2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted24f) mKuwait2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted24f2) #EDUC GE mKuwait2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted22b) mKuwait2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted22b2) mKuwait2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted22c) mKuwait2013weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted22c2) mKuwait2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted22d) mKuwait2013weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted22d2) mKuwait2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted22e) mKuwait2013weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted22e2) mKuwait2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted22f) mKuwait2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted22f2) #POL GE mKuwait20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Kuwait2013nomiss) summary(mKuwait20132b) mKuwait20132b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Kuwait2013nomiss) summary(mKuwait20132b2) mKuwait2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted23c) mKuwait2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted23d) mKuwait2013weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted23d2) mKuwait2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted23e) mKuwait2013weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted23e2) mKuwait2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted23f) mKuwait2013weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Kuwait2013weighted) summary(mKuwait2013weighted23f2) ################################################################################################################## ################################################################################################################## ########################################CREATING WEIGHTS LEBANON 2013############################################### ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Lebanon2013 <- ABWVS20132018[ABWVS20132018$country == "Lebanon" & ABWVS20132018$year == 2013,] table(Lebanon2013$country) table(Lebanon2013$year) Lebanon2013$weightcats <- 99999 table(Lebanon2013$weightcats) Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 1 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 2 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 3 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 4 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 5 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 6 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 7 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 8 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 9 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 10 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 11 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 12 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 13 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 14 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 15 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 16 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 17 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 18 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 19 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 20 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 21 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 22 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 23 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 0 ] <- 24 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 25 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 26 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 27 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 28 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 29 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 30 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 31 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 32 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 33 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 34 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 35 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 36 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 37 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 38 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 39 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 40 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 41 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 42 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 43 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 44 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 45 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 46 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 47 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 0 ] <- 48 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 49 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 50 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 51 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 52 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 53 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 54 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 55 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 56 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 57 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 58 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 59 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 60 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 61 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 62 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 63 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 64 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 65 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 66 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 67 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 68 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 69 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 70 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 71 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==0 & Lebanon2013$Surveytype == 1 ] <- 72 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 73 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 74 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 75 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 76 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 77 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 78 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 79 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 80 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 81 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 82 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 83 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==0 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 84 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 85 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 86 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 87 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 88 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 89 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==0 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 90 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 91 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==0 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 92 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 93 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==1 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 94 Lebanon2013$weightcats[Lebanon2013$gender == 0 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 95 Lebanon2013$weightcats[Lebanon2013$gender == 1 & Lebanon2013$agecats ==2 & Lebanon2013$Educcats ==1 & Lebanon2013$Mar2 ==1 & Lebanon2013$Empl ==1 & Lebanon2013$Surveytype == 1 ] <- 96 table(Lebanon2013$weightcats) Lebanon2013nomiss <- Lebanon2013[Lebanon2013$weightcats != 99999,] table(Lebanon2013nomiss$weightcats) prop.table(table(Lebanon2013nomiss$weightcats, Lebanon2013nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 1 7 9 15 23 Lebanon2013nomiss2 <- Lebanon2013nomiss[Lebanon2013nomiss$weightcats != 1 & Lebanon2013nomiss$weightcats != 7 & Lebanon2013nomiss$weightcats != 9 & Lebanon2013nomiss$weightcats != 15 & Lebanon2013nomiss$weightcats != 23 ,] table(Lebanon2013nomiss2$weightcats) Lebanon13.distribution <- data.frame(weightcats = c( "2", "3", "4", "5", "6", "8", "10", "11", "12", "13", "14", "16", "17", "18", "19", "20", "22", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "50", "51", "52", "53", "54", "56", "58", "59", "60", "61", "62", "64", "65", "66", "67", "68", "70", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "96"), Freq = nrow(Lebanon2013nomiss2) * c( 0.007569386, 0.0046257359, 0.0340622372, 0.0109335576, 0.0269133726, 0.0008410429, 0.0033641716, 0.0012615643, 0.0025231287, 0.0424726661, 0.0403700589, 0.0092514718, 0.0046257359, 0.0117746005, 0.0079899075, 0.0084104289, 0.0008410429, 0.0012615643, 0.0058873003, 0.0084104289, 0.0336417157, 0.0218671152, 0.0378469302, 0.0130361648, 0.0012615643, 0.0021026072, 0.007569386, 0.0088309504, 0.0071488646, 0.0012615643, 0.0281749369, 0.0142977292, 0.0206055509, 0.012195122, 0.0054667788, 0.0079899075, 0.012195122, 0.0088309504, 0.0050462574, 0.003784693, 0.0016820858, 0.0025231287, 0.007569386, 0.0046257359, 0.0340622372, 0.0109335576, 0.0269133726, 0.0008410429, 0.0033641716, 0.0012615643, 0.0025231287, 0.0424726661, 0.0403700589, 0.0092514718, 0.0046257359, 0.0117746005, 0.0079899075, 0.0084104289, 0.0008410429, 0.0012615643, 0.0058873003, 0.0084104289, 0.0336417157, 0.0218671152, 0.0378469302, 0.0130361648, 0.0012615643, 0.0021026072, 0.007569386, 0.0088309504, 0.0071488646, 0.0012615643, 0.0281749369, 0.0142977292, 0.0206055509, 0.012195122, 0.0054667788, 0.0079899075, 0.012195122, 0.0088309504, 0.0050462574, 0.003784693, 0.0016820858, 0.0025231287)) Lebanon2013w <- svydesign(ids=~1, data = Lebanon2013nomiss2) Lebanon2013weighted <- rake(design = Lebanon2013w, sample.margins = list(~weightcats), population.margins = list(Lebanon13.distribution)) table(Lebanon2013nomiss2$Surveytype) svytable(~Surveytype, Lebanon2013weighted) #WORKED. table(Lebanon2013nomiss2$gender) svytable(~gender, Lebanon2013weighted) #worked. table(Lebanon2013nomiss2$gender, Lebanon2013nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Lebanon2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## ###########################################ANALYSES LEBANON2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Lebanon2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Lebanon2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Lebanon2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Lebanon2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Lebanon2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Lebanon2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Lebanon2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Lebanon2013weighted, Surveytype ==1))) #TRUST mLebanon2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted, family = quasibinomial) summary(mLebanon2013weighted21b) mLebanon2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted, family = quasibinomial) summary(mLebanon2013weighted21c) mLebanon2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted, family = quasibinomial) summary(mLebanon2013weighted21c2) mLebanon2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted, family = quasibinomial) summary(mLebanon2013weighted21d) mLebanon2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted, family = quasibinomial) summary(mLebanon2013weighted21d2) mLebanon2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted, family = quasibinomial) summary(mLebanon2013weighted21e) mLebanon2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted, family = quasibinomial) summary(mLebanon2013weighted21e2) mLebanon2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted, family = quasibinomial) summary(mLebanon2013weighted21f) mLebanon2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted, family = quasibinomial) summary(mLebanon2013weighted21f2) #TRUST POL mLebanon20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon2013nomiss) summary(mLebanon20132b) mLebanon20132b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon2013nomiss) summary(mLebanon20132b2) mLebanon2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted24c) mLebanon2013weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted24c2) mLebanon2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted24d) mLebanon2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted24d2) mLebanon2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted24e) mLebanon2013weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted24e2) mLebanon2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted24f) mLebanon2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted24f2) #EDUC GE mLebanon2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted22b) mLebanon2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted22b2) mLebanon2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted22c) mLebanon2013weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted22c2) mLebanon2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted22d) mLebanon2013weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted22d2) mLebanon2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted22e) mLebanon2013weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted22e2) mLebanon2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted22f) mLebanon2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted22f2) #POL GE mLebanon20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon2013nomiss) summary(mLebanon20132b) mLebanon20132b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon2013nomiss) summary(mLebanon20132b2) mLebanon2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted23c) mLebanon2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted23d) mLebanon2013weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted23d2) mLebanon2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted23e) mLebanon2013weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted23e2) mLebanon2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted23f) mLebanon2013weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2013weighted) summary(mLebanon2013weighted23f2) ################################################################################################################## ################################################################################################################## ########################################CREATING WEIGHTS LIBYA 2013############################################### ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Libya2013 <- ABWVS20132018[ABWVS20132018$country == "Libya" & ABWVS20132018$year == 2013,] table(Libya2013$country) table(Libya2013$year) Libya2013$weightcats <- 99999 table(Libya2013$weightcats) Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 1 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 2 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 3 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 4 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 5 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 6 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 7 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 8 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 9 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 10 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 11 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 12 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 13 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 14 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 15 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 16 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 17 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 18 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 19 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 20 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 21 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 22 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 23 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 0 ] <- 24 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 25 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 26 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 27 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 28 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 29 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 30 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 31 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 32 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 33 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 34 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 35 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 36 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 37 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 38 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 39 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 40 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 41 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 42 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 43 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 44 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 45 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 46 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 47 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 0 ] <- 48 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 49 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 50 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 51 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 52 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 53 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 54 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 55 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 56 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 57 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 58 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 59 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 60 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 61 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 62 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 63 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 64 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 65 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 66 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 67 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 68 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 69 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 70 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 71 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==0 & Libya2013$Surveytype == 1 ] <- 72 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 73 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 74 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 75 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 76 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 77 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 78 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 79 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 80 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 81 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 82 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 83 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==0 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 84 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 85 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 86 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 87 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 88 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 89 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==0 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 90 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 91 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==0 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 92 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 93 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==1 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 94 Libya2013$weightcats[Libya2013$gender == 0 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 95 Libya2013$weightcats[Libya2013$gender == 1 & Libya2013$agecats ==2 & Libya2013$Educcats ==1 & Libya2013$Mar2 ==1 & Libya2013$Empl ==1 & Libya2013$Surveytype == 1 ] <- 96 table(Libya2013$weightcats) Libya2013nomiss <- Libya2013[Libya2013$weightcats != 99999,] table(Libya2013nomiss$weightcats) prop.table(table(Libya2013nomiss$weightcats, Libya2013nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 1 7 41 Libya2013nomiss2 <- Libya2013nomiss[Libya2013nomiss$weightcats != 1 & Libya2013nomiss$weightcats != 7 & Libya2013nomiss$weightcats != 41 ,] table(Libya2013nomiss2$weightcats) Libya13.distribution <- data.frame(weightcats = c( "2", "3", "4", "5", "6", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "42", "43", "44", "45", "46", "48", "50", "51", "52", "53", "54", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "90", "91", "92", "93", "94", "96"), Freq = nrow(Libya2013nomiss2) * c(0.0157099698, 0.0069486405, 0.0561933535, 0.0181268882, 0.0362537764, 0.0060422961, 0.0021148036, 0.0090634441, 0.0048338369, 0.0012084592, 0.0504531722, 0.035347432, 0.0024169184, 0.0190332326, 0.0027190332, 0.0160120846, 0.0054380665, 0.0048338369, 0.0012084592, 0.0027190332, 0.0006042296, 0.0003021148, 0.0084592145, 0.0042296073, 0.0628398792, 0.0326283988, 0.0259818731, 0.0102719033, 0.0054380665, 0.0033232628, 0.0359516616, 0.0208459215, 0.0087613293, 0.0012084592, 0.0332326284, 0.0042296073, 0.0163141994, 0.0114803625, 0.0036253776, 0.0105740181, 0.0048338369, 0.0111782477, 0.0117824773, 0.0018126888, 0.0157099698, 0.0069486405, 0.0561933535, 0.0181268882, 0.0362537764, 0.0060422961, 0.0021148036, 0.0090634441, 0.0048338369, 0.0012084592, 0.0504531722, 0.035347432, 0.0024169184, 0.0190332326, 0.0027190332, 0.0160120846, 0.0054380665, 0.0048338369, 0.0012084592, 0.0027190332, 0.0006042296, 0.0003021148, 0.0084592145, 0.0042296073, 0.0628398792, 0.0326283988, 0.0259818731, 0.0102719033, 0.0054380665, 0.0033232628, 0.0359516616, 0.0208459215, 0.0087613293, 0.0012084592, 0.0332326284, 0.0042296073, 0.0163141994, 0.0114803625, 0.0036253776, 0.0105740181, 0.0048338369, 0.0111782477, 0.0117824773, 0.0018126888)) Libya2013w <- svydesign(ids=~1, data = Libya2013nomiss2) Libya2013weighted <- rake(design = Libya2013w, sample.margins = list(~weightcats), population.margins = list(Libya13.distribution)) table(Libya2013nomiss2$Surveytype) svytable(~Surveytype, Libya2013weighted) #WORKED. table(Libya2013nomiss2$gender) svytable(~gender, Libya2013weighted) #worked. table(Libya2013nomiss2$gender, Libya2013nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Libya2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## ###########################################ANALYSES LIBYA2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Libya2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Libya2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Libya2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Libya2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Libya2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Libya2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Libya2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Libya2013weighted, Surveytype ==1))) #TRUST mLibya2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted, family = quasibinomial) summary(mLibya2013weighted21b) mLibya2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted, family = quasibinomial) summary(mLibya2013weighted21c) mLibya2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted, family = quasibinomial) summary(mLibya2013weighted21c2) mLibya2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted, family = quasibinomial) summary(mLibya2013weighted21d) mLibya2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted, family = quasibinomial) summary(mLibya2013weighted21d2) mLibya2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted, family = quasibinomial) summary(mLibya2013weighted21e) mLibya2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted, family = quasibinomial) summary(mLibya2013weighted21e2) mLibya2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted, family = quasibinomial) summary(mLibya2013weighted21f) mLibya2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted, family = quasibinomial) summary(mLibya2013weighted21f2) #TRUST POL mLibya20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Libya2013nomiss) summary(mLibya20132b) mLibya20132b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Libya2013nomiss) summary(mLibya20132b2) mLibya2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted24c) mLibya2013weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted24c2) mLibya2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted24d) mLibya2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted24d2) mLibya2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted24e) mLibya2013weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted24e2) mLibya2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted24f) mLibya2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted24f2) #EDUC GE mLibya2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted22b) mLibya2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted22b2) mLibya2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted22c) mLibya2013weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted22c2) mLibya2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted22d) mLibya2013weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted22d2) mLibya2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted22e) mLibya2013weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted22e2) mLibya2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted22f) mLibya2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted22f2) #POL GE mLibya20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Libya2013nomiss) summary(mLibya20132b) mLibya20132b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Libya2013nomiss) summary(mLibya20132b2) mLibya2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted23c) mLibya2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted23d) mLibya2013weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted23d2) mLibya2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted23e) mLibya2013weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted23e2) mLibya2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted23f) mLibya2013weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Libya2013weighted) summary(mLibya2013weighted23f2) ################################################################################################################## ################################################################################################################## ########################################CREATING WEIGHTS MOROCCO 2013############################################### ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Morocco2013 <- ABWVS20132018[ABWVS20132018$country == "Morocco" & ABWVS20132018$year == 2013,] table(Morocco2013$country) table(Morocco2013$year) Morocco2013$weightcats <- 99999 table(Morocco2013$weightcats) table(Morocco2013$Educcats, Morocco2013$Surveytype) #Very few people in morocco are educcat 1 (tertiary), in both datasets, but expecially in WVS Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 1 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 2 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 3 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 4 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 5 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 6 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 7 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 8 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 9 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 10 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 11 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 12 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 13 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 14 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 15 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 16 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 17 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 18 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 19 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 20 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 21 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 22 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 23 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 0 ] <- 24 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 25 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 26 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 27 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 28 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 29 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 30 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 31 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 32 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 33 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 34 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 35 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 36 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 37 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 38 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 39 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 40 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 41 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 42 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 43 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 44 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 45 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 46 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 47 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 0 ] <- 48 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 49 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 50 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 51 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 52 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 53 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 54 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 55 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 56 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 57 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 58 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 59 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 60 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 61 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 62 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 63 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 64 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 65 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 66 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 67 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 68 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 69 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 70 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 71 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==0 & Morocco2013$Surveytype == 1 ] <- 72 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 73 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 74 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 75 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 76 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 77 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 78 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 79 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 80 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 81 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 82 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 83 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==0 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 84 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 85 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 86 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 87 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 88 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 89 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==0 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 90 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 91 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==0 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 92 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 93 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==1 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 94 Morocco2013$weightcats[Morocco2013$gender == 0 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 95 Morocco2013$weightcats[Morocco2013$gender == 1 & Morocco2013$agecats ==2 & Morocco2013$Educcats ==1 & Morocco2013$Mar2 ==1 & Morocco2013$Empl ==1 & Morocco2013$Surveytype == 1 ] <- 96 table(Morocco2013$weightcats) Morocco2013nomiss <- Morocco2013[Morocco2013$weightcats != 99999,] table(Morocco2013nomiss$weightcats) #THERE ARE HUGE GAPING HOLES IN MOROCCO IN 7-12 (and 55-60) AND IN 19-24 (and 67-72) #which are probably due to educ = 1 being very low in morocco in both datasets. #the educ = 1 ones that there are, are in 31-34 (and 79-84) and in 34, 44, 46 (and 91-95) #so it's a combo of educ =1 with another thing (and that other thing is not gender or age) #it's employment. There are almost no people in surveys in morocco (either ab or wvs) who are both tertiary educated AND non-employed. table(Morocco2013$Educcats, Morocco2013$Empl) prop.table(table(Morocco2013nomiss$weightcats, Morocco2013nomiss$Surveytype)) #anyways, cats without respondents in their accompanying WVS/AB countercategory: 83 84 93 95 Morocco2013nomiss2 <- Morocco2013nomiss[Morocco2013nomiss$weightcats != 83 & Morocco2013nomiss$weightcats != 84 & Morocco2013nomiss$weightcats != 93 & Morocco2013nomiss$weightcats != 95 ,] table(Morocco2013nomiss2$weightcats) Morocco13.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "13", "14", "15", "16", "17", "18", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "37", "38", "39", "40", "41", "42", "43", "44", "46", "49", "50", "51", "52", "53", "54", "61", "62", "63", "64", "65", "66", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "85", "86", "87", "88", "89", "90", "91", "92", "94"), Freq = nrow(Morocco2013nomiss2) * c( 0.0004319654, 0.0012958963, 0.0021598272, 0.0107991361, 0.0025917927, 0.0172786177, 0.0177105832, 0.0168466523, 0.0030237581, 0.0051835853, 0.0034557235, 0.0064794816, 0.0250539957, 0.0194384449, 0.0678185745, 0.0613390929, 0.0410367171, 0.0220302376, 0.0021598272, 0.0012958963, 0.0021598272, 0.0021598272, 0.0596112311, 0.0652267819, 0.0211663067, 0.0198704104, 0.007775378, 0.0086393089, 0.0008639309, 0.0021598272, 0.0008639309, 0.0004319654, 0.0012958963, 0.0021598272, 0.0107991361, 0.0025917927, 0.0172786177, 0.0177105832, 0.0168466523, 0.0030237581, 0.0051835853, 0.0034557235, 0.0064794816, 0.0250539957, 0.0194384449, 0.0678185745, 0.0613390929, 0.0410367171, 0.0220302376, 0.0021598272, 0.0012958963, 0.0021598272, 0.0021598272, 0.0596112311, 0.0652267819, 0.0211663067, 0.0198704104, 0.007775378, 0.0086393089, 0.0008639309, 0.0021598272, 0.0008639309)) Morocco2013w <- svydesign(ids=~1, data = Morocco2013nomiss2) Morocco2013weighted <- rake(design = Morocco2013w, sample.margins = list(~weightcats), population.margins = list(Morocco13.distribution)) table(Morocco2013nomiss2$Surveytype) svytable(~Surveytype, Morocco2013weighted) #WORKED. table(Morocco2013nomiss2$gender) svytable(~gender, Morocco2013weighted) #worked. table(Morocco2013nomiss2$gender, Morocco2013nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Morocco2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## ###########################################ANALYSES MOROCCO2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Morocco2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Morocco2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Morocco2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Morocco2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Morocco2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Morocco2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Morocco2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Morocco2013weighted, Surveytype ==1))) #TRUST mMorocco2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted, family = quasibinomial) summary(mMorocco2013weighted21b) mMorocco2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted, family = quasibinomial) summary(mMorocco2013weighted21c) mMorocco2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted, family = quasibinomial) summary(mMorocco2013weighted21c2) mMorocco2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted, family = quasibinomial) summary(mMorocco2013weighted21d) mMorocco2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted, family = quasibinomial) summary(mMorocco2013weighted21d2) mMorocco2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted, family = quasibinomial) summary(mMorocco2013weighted21e) mMorocco2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted, family = quasibinomial) summary(mMorocco2013weighted21e2) mMorocco2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted, family = quasibinomial) summary(mMorocco2013weighted21f) mMorocco2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted, family = quasibinomial) summary(mMorocco2013weighted21f2) #TRUST POL mMorocco20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Morocco2013nomiss) summary(mMorocco20132b) mMorocco20132b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Morocco2013nomiss) summary(mMorocco20132b2) mMorocco2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted24c) mMorocco2013weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted24c2) mMorocco2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted24d) mMorocco2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted24d2) mMorocco2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted24e) mMorocco2013weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted24e2) mMorocco2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted24f) mMorocco2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted24f2) #EDUC GE mMorocco2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted22b) mMorocco2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted22b2) mMorocco2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted22c) mMorocco2013weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted22c2) mMorocco2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted22d) mMorocco2013weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted22d2) mMorocco2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted22e) mMorocco2013weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted22e2) mMorocco2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted22f) mMorocco2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted22f2) #POL GE mMorocco20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Morocco2013nomiss) summary(mMorocco20132b) mMorocco20132b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Morocco2013nomiss) summary(mMorocco20132b2) mMorocco2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted23c) mMorocco2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted23d) mMorocco2013weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted23d2) mMorocco2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted23e) mMorocco2013weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted23e2) mMorocco2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted23f) mMorocco2013weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Morocco2013weighted) summary(mMorocco2013weighted23f2) ################################################################################################################## ################################################################################################################## #######################################CREATING WEIGHTS PALESTINE 2013############################################ ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Palestine2013 <- ABWVS20132018[ABWVS20132018$country == "Palestine" & ABWVS20132018$year == 2013,] table(Palestine2013$country) table(Palestine2013$year) Palestine2013$weightcats <- 99999 table(Palestine2013$weightcats) Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 1 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 2 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 3 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 4 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 5 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 6 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 7 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 8 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 9 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 10 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 11 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 12 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 13 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 14 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 15 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 16 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 17 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 18 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 19 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 20 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 21 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 22 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 23 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 0 ] <- 24 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 25 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 26 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 27 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 28 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 29 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 30 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 31 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 32 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 33 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 34 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 35 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 36 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 37 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 38 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 39 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 40 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 41 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 42 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 43 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 44 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 45 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 46 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 47 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 0 ] <- 48 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 49 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 50 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 51 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 52 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 53 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 54 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 55 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 56 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 57 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 58 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 59 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 60 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 61 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 62 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 63 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 64 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 65 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 66 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 67 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 68 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 69 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 70 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 71 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==0 & Palestine2013$Surveytype == 1 ] <- 72 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 73 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 74 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 75 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 76 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 77 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 78 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 79 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 80 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 81 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 82 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 83 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==0 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 84 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 85 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 86 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 87 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 88 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 89 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==0 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 90 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 91 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==0 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 92 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 93 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==1 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 94 Palestine2013$weightcats[Palestine2013$gender == 0 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 95 Palestine2013$weightcats[Palestine2013$gender == 1 & Palestine2013$agecats ==2 & Palestine2013$Educcats ==1 & Palestine2013$Mar2 ==1 & Palestine2013$Empl ==1 & Palestine2013$Surveytype == 1 ] <- 96 table(Palestine2013$weightcats) Palestine2013nomiss <- Palestine2013[Palestine2013$weightcats != 99999,] table(Palestine2013nomiss$weightcats) prop.table(table(Palestine2013nomiss$weightcats, Palestine2013nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 55 57 69 22 71 47 48 Palestine2013nomiss2 <- Palestine2013nomiss[Palestine2013nomiss$weightcats != 55 & Palestine2013nomiss$weightcats != 57 & Palestine2013nomiss$weightcats != 69 & Palestine2013nomiss$weightcats != 22 & Palestine2013nomiss$weightcats != 71 & Palestine2013nomiss$weightcats != 47 & Palestine2013nomiss$weightcats != 48,] table(Palestine2013nomiss2$weightcats) Palestine13.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "8", "10", "11", "13", "14", "15", "16", "17", "18", "19", "20", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "49", "50", "51", "52", "53", "54", "56", "58", "59", "61", "62", "63", "64", "65", "66", "67", "68", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94"), Freq = nrow(Palestine2013nomiss2) * c( 0.0027422303, 0.0278793419, 0.0123400366, 0.071297989, 0.0201096892, 0.018738574, 0.0073126143, 0.0054844607, 0.0027422303, 0.0319926874, 0.0383912249, 0.0004570384, 0.0114259598, 0.0027422303, 0.0114259598, 0.0054844607, 0.0073126143, 0.0013711152, 0.0118829982, 0.0031992687, 0.0557586837, 0.0050274223, 0.0169104205, 0.0009140768, 0.0059414991, 0.0045703839, 0.0214808044, 0.0095978062, 0.0045703839, 0.0004570384, 0.0173674589, 0.0009140768, 0.0018281536, 0.0018281536, 0.0009140768, 0.0004570384, 0.0059414991, 0.0036563071, 0.0009140768, 0.0013711152, 0.0027422303, 0.0278793419, 0.0123400366, 0.071297989, 0.0201096892, 0.018738574, 0.0073126143, 0.0054844607, 0.0027422303, 0.0319926874, 0.0383912249, 0.0004570384, 0.0114259598, 0.0027422303, 0.0114259598, 0.0054844607, 0.0073126143, 0.0013711152, 0.0118829982, 0.0031992687, 0.0557586837, 0.0050274223, 0.0169104205, 0.0009140768, 0.0059414991, 0.0045703839, 0.0214808044, 0.0095978062, 0.0045703839, 0.0004570384, 0.0173674589, 0.0009140768, 0.0018281536, 0.0018281536, 0.0009140768, 0.0004570384, 0.0059414991, 0.0036563071, 0.0009140768, 0.0013711152)) Palestine2013w <- svydesign(ids=~1, data = Palestine2013nomiss2) Palestine2013weighted <- rake(design = Palestine2013w, sample.margins = list(~weightcats), population.margins = list(Palestine13.distribution)) table(Palestine2013nomiss2$Surveytype) svytable(~Surveytype, Palestine2013weighted) #WORKED. table(Palestine2013nomiss2$gender) svytable(~gender, Palestine2013weighted) #worked. table(Palestine2013nomiss2$gender, Palestine2013nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Palestine2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## #######################################ANALYSES PALESTINE2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Palestine2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Palestine2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Palestine2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Palestine2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Palestine2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Palestine2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Palestine2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Palestine2013weighted, Surveytype ==1))) #TRUST mPalestine2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted, family = quasibinomial) summary(mPalestine2013weighted21b) mPalestine2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted, family = quasibinomial) summary(mPalestine2013weighted21c) mPalestine2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted, family = quasibinomial) summary(mPalestine2013weighted21c2) mPalestine2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted, family = quasibinomial) summary(mPalestine2013weighted21d) mPalestine2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted, family = quasibinomial) summary(mPalestine2013weighted21d2) mPalestine2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted, family = quasibinomial) summary(mPalestine2013weighted21e) mPalestine2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted, family = quasibinomial) summary(mPalestine2013weighted21e2) mPalestine2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted, family = quasibinomial) summary(mPalestine2013weighted21f) mPalestine2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted, family = quasibinomial) summary(mPalestine2013weighted21f2) #TRUST POL mPalestine20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Palestine2013nomiss) summary(mPalestine20132b) mPalestine20132b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Palestine2013nomiss) summary(mPalestine20132b2) mPalestine2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted24c) mPalestine2013weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted24c2) mPalestine2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted24d) mPalestine2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted24d2) mPalestine2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted24e) mPalestine2013weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted24e2) mPalestine2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted24f) mPalestine2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted24f2) #EDUC GE mPalestine2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted22b) mPalestine2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted22b2) mPalestine2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted22c) mPalestine2013weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted22c2) mPalestine2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted22d) mPalestine2013weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted22d2) mPalestine2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted22e) mPalestine2013weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted22e2) mPalestine2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted22f) mPalestine2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted22f2) #POL GE mPalestine20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Palestine2013nomiss) summary(mPalestine20132b) mPalestine20132b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Palestine2013nomiss) summary(mPalestine20132b2) mPalestine2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted23c) mPalestine2013weighted23c2 <- svyglm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted23c2) mPalestine2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted23d) mPalestine2013weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted23d2) mPalestine2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted23e) mPalestine2013weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted23e2) mPalestine2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted23f) mPalestine2013weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Palestine2013weighted) summary(mPalestine2013weighted23f2) ################################################################################################################## ################################################################################################################## #########################################CREATING WEIGHTS TUNISIA 2013############################################ ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Tunisia2013 <- ABWVS20132018[ABWVS20132018$country == "Tunisia" & ABWVS20132018$year == 2013,] table(Tunisia2013$country) table(Tunisia2013$year) Tunisia2013$weightcats <- 99999 table(Tunisia2013$weightcats) Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 1 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 2 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 3 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 4 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 5 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 6 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 7 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 8 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 9 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 10 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 11 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 12 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 13 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 14 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 15 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 16 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 17 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 18 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 19 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 20 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 21 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 22 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 23 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 0 ] <- 24 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 25 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 26 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 27 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 28 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 29 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 30 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 31 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 32 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 33 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 34 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 35 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 36 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 37 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 38 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 39 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 40 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 41 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 42 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 43 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 44 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 45 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 46 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 47 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 0 ] <- 48 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 49 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 50 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 51 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 52 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 53 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 54 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 55 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 56 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 57 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 58 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 59 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 60 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 61 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 62 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 63 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 64 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 65 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 66 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 67 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 68 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 69 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 70 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 71 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==0 & Tunisia2013$Surveytype == 1 ] <- 72 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 73 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 74 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 75 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 76 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 77 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 78 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 79 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 80 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 81 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 82 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 83 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==0 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 84 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 85 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 86 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 87 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 88 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 89 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==0 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 90 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 91 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==0 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 92 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 93 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==1 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 94 Tunisia2013$weightcats[Tunisia2013$gender == 0 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 95 Tunisia2013$weightcats[Tunisia2013$gender == 1 & Tunisia2013$agecats ==2 & Tunisia2013$Educcats ==1 & Tunisia2013$Mar2 ==1 & Tunisia2013$Empl ==1 & Tunisia2013$Surveytype == 1 ] <- 96 table(Tunisia2013$weightcats) Tunisia2013nomiss <- Tunisia2013[Tunisia2013$weightcats != 99999,] table(Tunisia2013nomiss$weightcats) prop.table(table(Tunisia2013nomiss$weightcats, Tunisia2013nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 7 57 12 24 80 Tunisia2013nomiss2 <- Tunisia2013nomiss[Tunisia2013nomiss$weightcats != 7 & Tunisia2013nomiss$weightcats != 57 & Tunisia2013nomiss$weightcats != 12 & Tunisia2013nomiss$weightcats != 24 & Tunisia2013nomiss$weightcats != 80,] table(Tunisia2013nomiss2$weightcats) Tunisia13.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "8", "10", "11", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "25", "26", "27", "28", "29", "30", "31", "33", "34", "35", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "49", "50", "51", "52", "53", "54", "56", "58", "59", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "73", "74", "75", "76", "77", "78", "79", "81", "82", "83", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94"), Freq = nrow(Tunisia2013nomiss2) * c( 0.0021240442, 0.0080713679, 0.0063721325, 0.0429056924, 0.0382327952, 0.0437553101, 0.0012744265, 0.002548853, 0.0004248088, 0.0586236194, 0.0458793543, 0.0114698386, 0.0118946474, 0.0012744265, 0.0144435004, 0.0084961767, 0.0046728972, 0.0029736619, 0.0008496177, 0.0021240442, 0.005097706, 0.046728972, 0.0182667799, 0.0246389125, 0.0063721325, 0.0004248088, 0.0046728972, 0.0021240442, 0.0012744265, 0.0322854715, 0.0165675446, 0.0161427358, 0.0101954121, 0.0021240442, 0.0016992353, 0.0055225149, 0.0029736619, 0.002548853, 0.0021240442, 0.0021240442, 0.0080713679, 0.0063721325, 0.0429056924, 0.0382327952, 0.0437553101, 0.0012744265, 0.002548853, 0.0004248088, 0.0586236194, 0.0458793543, 0.0114698386, 0.0118946474, 0.0012744265, 0.0144435004, 0.0084961767, 0.0046728972, 0.0029736619, 0.0008496177, 0.0021240442, 0.005097706, 0.046728972, 0.0182667799, 0.0246389125, 0.0063721325, 0.0004248088, 0.0046728972, 0.0021240442, 0.0012744265, 0.0322854715, 0.0165675446, 0.0161427358, 0.0101954121, 0.0021240442, 0.0016992353, 0.0055225149, 0.0029736619, 0.002548853, 0.0021240442)) Tunisia2013w <- svydesign(ids=~1, data = Tunisia2013nomiss2) Tunisia2013weighted <- rake(design = Tunisia2013w, sample.margins = list(~weightcats), population.margins = list(Tunisia13.distribution)) table(Tunisia2013nomiss2$Surveytype) svytable(~Surveytype, Tunisia2013weighted) #WORKED. table(Tunisia2013nomiss2$gender) svytable(~gender, Tunisia2013weighted) #worked. table(Tunisia2013nomiss2$gender, Tunisia2013nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Tunisia2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## #########################################ANALYSES TUNISIA2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Tunisia2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Tunisia2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Tunisia2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Tunisia2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Tunisia2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Tunisia2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Tunisia2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Tunisia2013weighted, Surveytype ==1))) #TRUST mTunisia2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted, family = quasibinomial) summary(mTunisia2013weighted21b) mTunisia2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted, family = quasibinomial) summary(mTunisia2013weighted21c) mTunisia2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted, family = quasibinomial) summary(mTunisia2013weighted21c2) mTunisia2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted, family = quasibinomial) summary(mTunisia2013weighted21d) mTunisia2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted, family = quasibinomial) summary(mTunisia2013weighted21d2) mTunisia2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted, family = quasibinomial) summary(mTunisia2013weighted21e) mTunisia2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted, family = quasibinomial) summary(mTunisia2013weighted21e2) mTunisia2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted, family = quasibinomial) summary(mTunisia2013weighted21f) mTunisia2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted, family = quasibinomial) summary(mTunisia2013weighted21f2) #TRUST POL mTunisia20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia2013nomiss) summary(mTunisia20132b) mTunisia20132b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia2013nomiss) summary(mTunisia20132b2) mTunisia2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted24c) mTunisia2013weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted24c2) mTunisia2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted24d) mTunisia2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted24d2) mTunisia2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted24e) mTunisia2013weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted24e2) mTunisia2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted24f) mTunisia2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted24f2) #EDUC GE mTunisia2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted22b) mTunisia2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted22b2) mTunisia2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted22c) mTunisia2013weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted22c2) mTunisia2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted22d) mTunisia2013weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted22d2) mTunisia2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted22e) mTunisia2013weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted22e2) mTunisia2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted22f) mTunisia2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted22f2) #POL GE mTunisia20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia2013nomiss) summary(mTunisia20132b) mTunisia20132b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia2013nomiss) summary(mTunisia20132b2) mTunisia2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted23c) mTunisia2013weighted23c2 <- svyglm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted23c2) mTunisia2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted23d) mTunisia2013weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted23d2) mTunisia2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted23e) mTunisia2013weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted23e2) mTunisia2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted23f) mTunisia2013weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2013weighted) summary(mTunisia2013weighted23f2) ################################################################################################################## ################################################################################################################## #########################################CREATING WEIGHTS YEMEN 2013############################################ ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Yemen2013 <- ABWVS20132018[ABWVS20132018$country == "Yemen" & ABWVS20132018$year == 2013,] table(Yemen2013$country) table(Yemen2013$year) Yemen2013$weightcats <- 99999 table(Yemen2013$weightcats) Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 1 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 2 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 3 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 4 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 5 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 6 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 7 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 8 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 9 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 10 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 11 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 12 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 13 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 14 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 15 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 16 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 17 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 18 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 19 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 20 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 21 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 22 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 23 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 0 ] <- 24 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 25 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 26 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 27 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 28 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 29 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 30 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 31 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 32 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 33 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 34 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 35 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 36 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 37 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 38 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 39 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 40 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 41 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 42 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 43 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 44 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 45 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 46 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 47 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 0 ] <- 48 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 49 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 50 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 51 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 52 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 53 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 54 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 55 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 56 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 57 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 58 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 59 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 60 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 61 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 62 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 63 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 64 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 65 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 66 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 67 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 68 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 69 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 70 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 71 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==0 & Yemen2013$Surveytype == 1 ] <- 72 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 73 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 74 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 75 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 76 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 77 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 78 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 79 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 80 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 81 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 82 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 83 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==0 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 84 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 85 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 86 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 87 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 88 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 89 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==0 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 90 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 91 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==0 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 92 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 93 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==1 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 94 Yemen2013$weightcats[Yemen2013$gender == 0 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 95 Yemen2013$weightcats[Yemen2013$gender == 1 & Yemen2013$agecats ==2 & Yemen2013$Educcats ==1 & Yemen2013$Mar2 ==1 & Yemen2013$Empl ==1 & Yemen2013$Surveytype == 1 ] <- 96 table(Yemen2013$weightcats) Yemen2013nomiss <- Yemen2013[Yemen2013$weightcats != 99999,] table(Yemen2013nomiss$weightcats) prop.table(table(Yemen2013nomiss$weightcats, Yemen2013nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 11 20 22 80 Yemen2013nomiss2 <- Yemen2013nomiss[Yemen2013nomiss$weightcats != 11 & Yemen2013nomiss$weightcats != 20 & Yemen2013nomiss$weightcats != 22 & Yemen2013nomiss$weightcats != 80,] table(Yemen2013nomiss2$weightcats) Yemen13.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "13", "14", "15", "16", "17", "18", "19", "21", "25", "26", "27", "28", "29", "30", "31", "33", "34", "35", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "61", "62", "63", "64", "65", "66", "67", "69", "73", "74", "75", "76", "77", "78", "79", "81", "82", "83", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94"), Freq = nrow(Yemen2013nomiss2) * c( 0.0068399453, 0.0560875513, 0.0127678979, 0.0793433653, 0.0196078431, 0.0205198358, 0.0018239854, 0.0004559964, 0.0022799818, 0.0013679891, 0.0186958504, 0.0287277702, 0.0009119927, 0.0104879161, 0.0022799818, 0.0109439124, 0.0009119927, 0.0009119927, 0.0269037848, 0.0018239854, 0.07250342, 0.0054719562, 0.0205198358, 0.0004559964, 0.0031919745, 0.0200638395, 0.0022799818, 0.0009119927, 0.0127678979, 0.0027359781, 0.0009119927, 0.0031919745, 0.0009119927, 0.0004559964, 0.0004559964, 0.0013679891, 0.0004559964, 0.0004559964, 0.0068399453, 0.0560875513, 0.0127678979, 0.0793433653, 0.0196078431, 0.0205198358, 0.0018239854, 0.0004559964, 0.0022799818, 0.0013679891, 0.0186958504, 0.0287277702, 0.0009119927, 0.0104879161, 0.0022799818, 0.0109439124, 0.0009119927, 0.0009119927, 0.0269037848, 0.0018239854, 0.07250342, 0.0054719562, 0.0205198358, 0.0004559964, 0.0031919745, 0.0200638395, 0.0022799818, 0.0009119927, 0.0127678979, 0.0027359781, 0.0009119927, 0.0031919745, 0.0009119927, 0.0004559964, 0.0004559964, 0.0013679891, 0.0004559964, 0.0004559964)) Yemen2013w <- svydesign(ids=~1, data = Yemen2013nomiss2) Yemen2013weighted <- rake(design = Yemen2013w, sample.margins = list(~weightcats), population.margins = list(Yemen13.distribution)) table(Yemen2013nomiss2$Surveytype) svytable(~Surveytype, Yemen2013weighted) #WORKED. table(Yemen2013nomiss2$gender) svytable(~gender, Yemen2013weighted) #worked. table(Yemen2013nomiss2$gender, Yemen2013nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Yemen2013weighted) #WORKED. ################################################################################################################## ################################################################################################################## #########################################ANALYSES YEMEN2013 WEIGHTED############################################ ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Yemen2013weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Yemen2013weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Yemen2013weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Yemen2013weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Yemen2013weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Yemen2013weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Yemen2013weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Yemen2013weighted, Surveytype ==1))) #TRUST mYemen2013weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted, family = quasibinomial) summary(mYemen2013weighted21b) mYemen2013weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted, family = quasibinomial) summary(mYemen2013weighted21c) mYemen2013weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted, family = quasibinomial) summary(mYemen2013weighted21c2) mYemen2013weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted, family = quasibinomial) summary(mYemen2013weighted21d) mYemen2013weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted, family = quasibinomial) summary(mYemen2013weighted21d2) mYemen2013weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted, family = quasibinomial) summary(mYemen2013weighted21e) mYemen2013weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted, family = quasibinomial) summary(mYemen2013weighted21e2) mYemen2013weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted, family = quasibinomial) summary(mYemen2013weighted21f) mYemen2013weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted, family = quasibinomial) summary(mYemen2013weighted21f2) #TRUST POL mYemen20132b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Yemen2013nomiss) summary(mYemen20132b) mYemen20132b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Yemen2013nomiss) summary(mYemen20132b2) mYemen2013weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted24c) mYemen2013weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted24c2) mYemen2013weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted24d) mYemen2013weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted24d2) mYemen2013weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted24e) mYemen2013weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted24e2) mYemen2013weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted24f) mYemen2013weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted24f2) #EDUC GE mYemen2013weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted22b) mYemen2013weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted22b2) mYemen2013weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted22c) mYemen2013weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted22c2) mYemen2013weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted22d) mYemen2013weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted22d2) mYemen2013weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted22e) mYemen2013weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted22e2) mYemen2013weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted22f) mYemen2013weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted22f2) #POL GE mYemen20132b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Yemen2013nomiss) summary(mYemen20132b) mYemen20132b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Yemen2013nomiss) summary(mYemen20132b2) mYemen2013weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted23c) mYemen2013weighted23c2 <- svyglm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted23c2) mYemen2013weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted23d) mYemen2013weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted23d2) mYemen2013weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted23e) mYemen2013weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted23e2) mYemen2013weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted23f) mYemen2013weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Yemen2013weighted) summary(mYemen2013weighted23f2) ################################################################################################################## ################################################################################################################## ###########################################CREATING WEIGHTS EGYPT 2013############################################ ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Egypt2018 <- ABWVS20132018[ABWVS20132018$country == "Egypt" & ABWVS20132018$year == 2018,] table(Egypt2018$country) table(Egypt2018$year) Egypt2018$weightcats <- 99999 table(Egypt2018$weightcats) Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 1 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 2 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 3 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 4 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 5 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 6 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 7 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 8 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 9 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 10 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 11 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 12 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 13 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 14 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 15 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 16 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 17 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 18 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 19 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 20 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 21 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 22 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 23 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 0 ] <- 24 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 25 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 26 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 27 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 28 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 29 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 30 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 31 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 32 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 33 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 34 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 35 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 36 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 37 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 38 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 39 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 40 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 41 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 42 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 43 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 44 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 45 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 46 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 47 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 0 ] <- 48 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 49 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 50 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 51 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 52 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 53 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 54 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 55 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 56 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 57 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 58 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 59 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 60 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 61 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 62 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 63 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 64 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 65 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 66 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 67 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 68 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 69 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 70 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 71 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==0 & Egypt2018$Surveytype == 1 ] <- 72 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 73 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 74 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 75 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 76 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 77 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 78 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 79 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 80 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 81 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 82 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 83 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==0 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 84 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 85 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 86 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 87 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 88 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 89 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==0 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 90 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 91 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==0 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 92 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 93 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==1 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 94 Egypt2018$weightcats[Egypt2018$gender == 0 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 95 Egypt2018$weightcats[Egypt2018$gender == 1 & Egypt2018$agecats ==2 & Egypt2018$Educcats ==1 & Egypt2018$Mar2 ==1 & Egypt2018$Empl ==1 & Egypt2018$Surveytype == 1 ] <- 96 table(Egypt2018$weightcats) Egypt2018nomiss <- Egypt2018[Egypt2018$weightcats != 99999,] table(Egypt2018nomiss$weightcats) prop.table(table(Egypt2018nomiss$weightcats, Egypt2018nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 47 69 Egypt2018nomiss2 <- Egypt2018nomiss[Egypt2018nomiss$weightcats != 47 & Egypt2018nomiss$weightcats != 69,] table(Egypt2018nomiss2$weightcats) Egypt18.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "96"), Freq = nrow(Egypt2018nomiss2) * c( 0.0005621135, 0.0168634064, 0.0036537381, 0.0472175379, 0.0081506464, 0.0154581225, 0.0002810568, 0.0059021922, 0.0002810568, 0.00871276, 0.0016863406, 0.0019673974, 0.0129286116, 0.0106801574, 0.0002810568, 0.0092748735, 0.0019673974, 0.0163012929, 0.0028105677, 0.0016863406, 0.0014052839, 0.0005621135, 0.0005621135, 0.0075885329, 0.0011242271, 0.0486228218, 0.0047779651, 0.0269814503, 0.002529511, 0.0016863406, 0.0019673974, 0.0137717819, 0.0047779651, 0.00871276, 0.0008431703, 0.0151770658, 0.0011242271, 0.0059021922, 0.0036537381, 0.0030916245, 0.0022484542, 0.0053400787, 0.0014052839, 0.0036537381, 0.0016863406, 0.0005621135, 0.0005621135, 0.0168634064, 0.0036537381, 0.0472175379, 0.0081506464, 0.0154581225, 0.0002810568, 0.0059021922, 0.0002810568, 0.00871276, 0.0016863406, 0.0019673974, 0.0129286116, 0.0106801574, 0.0002810568, 0.0092748735, 0.0019673974, 0.0163012929, 0.0028105677, 0.0016863406, 0.0014052839, 0.0005621135, 0.0005621135, 0.0075885329, 0.0011242271, 0.0486228218, 0.0047779651, 0.0269814503, 0.002529511, 0.0016863406, 0.0019673974, 0.0137717819, 0.0047779651, 0.00871276, 0.0008431703, 0.0151770658, 0.0011242271, 0.0059021922, 0.0036537381, 0.0030916245, 0.0022484542, 0.0053400787, 0.0014052839, 0.0036537381, 0.0016863406, 0.0005621135)) Egypt2018w <- svydesign(ids=~1, data = Egypt2018nomiss2) Egypt2018weighted <- rake(design = Egypt2018w, sample.margins = list(~weightcats), population.margins = list(Egypt18.distribution)) table(Egypt2018nomiss2$Surveytype) svytable(~Surveytype, Egypt2018weighted) #WORKED. table(Egypt2018nomiss2$gender) svytable(~gender, Egypt2018weighted) #worked. table(Egypt2018nomiss2$gender, Egypt2018nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Egypt2018weighted) #WORKED. ################################################################################################################## ################################################################################################################## #########################################ANALYSES EGYPT2018 WEIGHTED############################################## ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Egypt2018weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Egypt2018weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Egypt2018weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Egypt2018weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Egypt2018weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Egypt2018weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Egypt2018weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Egypt2018weighted, Surveytype ==1))) #TRUST mEgypt2018weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted, family = quasibinomial) summary(mEgypt2018weighted21b) mEgypt2018weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted, family = quasibinomial) summary(mEgypt2018weighted21c) mEgypt2018weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted, family = quasibinomial) summary(mEgypt2018weighted21c2) mEgypt2018weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted, family = quasibinomial) summary(mEgypt2018weighted21d) mEgypt2018weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted, family = quasibinomial) summary(mEgypt2018weighted21d2) mEgypt2018weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted, family = quasibinomial) summary(mEgypt2018weighted21e) mEgypt2018weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted, family = quasibinomial) summary(mEgypt2018weighted21e2) mEgypt2018weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted, family = quasibinomial) summary(mEgypt2018weighted21f) mEgypt2018weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted, family = quasibinomial) summary(mEgypt2018weighted21f2) #TRUST POL mEgypt20182b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt2018nomiss) summary(mEgypt20182b) mEgypt20182b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Egypt2018nomiss) summary(mEgypt20182b2) mEgypt2018weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted24c) mEgypt2018weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted24c2) mEgypt2018weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted24d) mEgypt2018weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted24d2) mEgypt2018weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted24e) mEgypt2018weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted24e2) mEgypt2018weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted24f) mEgypt2018weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted24f2) #EDUC GE mEgypt2018weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted22b) mEgypt2018weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted22b2) mEgypt2018weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted22c) mEgypt2018weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted22c2) mEgypt2018weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted22d) mEgypt2018weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted22d2) mEgypt2018weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted22e) mEgypt2018weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted22e2) mEgypt2018weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted22f) mEgypt2018weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted22f2) #POL GE mEgypt20182b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt2018nomiss) summary(mEgypt20182b) mEgypt20182b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Egypt2018nomiss) summary(mEgypt20182b2) mEgypt2018weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted23c) mEgypt2018weighted23c2 <- svyglm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted23c2) mEgypt2018weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted23d) mEgypt2018weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted23d2) mEgypt2018weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted23e) mEgypt2018weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted23e2) mEgypt2018weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted23f) mEgypt2018weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Egypt2018weighted) summary(mEgypt2018weighted23f2) ################################################################################################################## ################################################################################################################## ###########################################CREATING WEIGHTS IRAQ 2013############################################ ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Iraq2018 <- ABWVS20132018[ABWVS20132018$country == "Iraq" & ABWVS20132018$year == 2018,] table(Iraq2018$country) table(Iraq2018$year) Iraq2018$weightcats <- 99999 table(Iraq2018$weightcats) Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 1 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 2 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 3 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 4 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 5 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 6 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 7 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 8 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 9 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 10 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 11 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 12 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 13 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 14 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 15 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 16 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 17 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 18 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 19 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 20 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 21 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 22 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 23 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 0 ] <- 24 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 25 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 26 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 27 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 28 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 29 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 30 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 31 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 32 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 33 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 34 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 35 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 36 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 37 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 38 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 39 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 40 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 41 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 42 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 43 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 44 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 45 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 46 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 47 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 0 ] <- 48 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 49 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 50 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 51 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 52 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 53 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 54 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 55 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 56 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 57 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 58 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 59 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 60 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 61 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 62 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 63 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 64 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 65 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 66 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 67 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 68 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 69 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 70 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 71 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==0 & Iraq2018$Surveytype == 1 ] <- 72 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 73 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 74 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 75 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 76 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 77 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 78 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 79 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 80 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 81 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 82 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 83 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==0 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 84 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 85 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 86 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 87 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 88 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 89 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==0 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 90 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 91 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==0 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 92 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 93 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==1 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 94 Iraq2018$weightcats[Iraq2018$gender == 0 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 95 Iraq2018$weightcats[Iraq2018$gender == 1 & Iraq2018$agecats ==2 & Iraq2018$Educcats ==1 & Iraq2018$Mar2 ==1 & Iraq2018$Empl ==1 & Iraq2018$Surveytype == 1 ] <- 96 table(Iraq2018$weightcats) Iraq2018nomiss <- Iraq2018[Iraq2018$weightcats != 99999,] table(Iraq2018nomiss$weightcats) prop.table(table(Iraq2018nomiss$weightcats, Iraq2018nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 23 Iraq2018nomiss2 <- Iraq2018nomiss[Iraq2018nomiss$weightcats != 23,] table(Iraq2018nomiss2$weightcats) Iraq18.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "96"), Freq = nrow(Iraq2018nomiss2) * c( 0.0047078372, 0.0238161174, 0.0102464691, 0.0373857657, 0.0124619219, 0.0166158959, 0.0005538632, 0.0049847688, 0.0024923844, 0.0060924952, 0.0008307948, 0.001384658, 0.0168928275, 0.0155081695, 0.0016615896, 0.005538632, 0.0016615896, 0.005538632, 0.0066463583, 0.005538632, 0.001384658, 0.0005538632, 0.0005538632, 0.0160620327, 0.005538632, 0.0257546386, 0.0060924952, 0.0141235115, 0.0033231792, 0.004153974, 0.0030462476, 0.0210468014, 0.0124619219, 0.0024923844, 0.0002769316, 0.0099695375, 0.001384658, 0.0022154528, 0.0005538632, 0.001384658, 0.0016615896, 0.004153974, 0.002769316, 0.0016615896, 0.0002769316, 0.0008307948, 0.0005538632, 0.0047078372, 0.0238161174, 0.0102464691, 0.0373857657, 0.0124619219, 0.0166158959, 0.0005538632, 0.0049847688, 0.0024923844, 0.0060924952, 0.0008307948, 0.001384658, 0.0168928275, 0.0155081695, 0.0016615896, 0.005538632, 0.0016615896, 0.005538632, 0.0066463583, 0.005538632, 0.001384658, 0.0005538632, 0.0005538632, 0.0160620327, 0.005538632, 0.0257546386, 0.0060924952, 0.0141235115, 0.0033231792, 0.004153974, 0.0030462476, 0.0210468014, 0.0124619219, 0.0024923844, 0.0002769316, 0.0099695375, 0.001384658, 0.0022154528, 0.0005538632, 0.001384658, 0.0016615896, 0.004153974, 0.002769316, 0.0016615896, 0.0002769316, 0.0008307948, 0.0005538632)) Iraq2018w <- svydesign(ids=~1, data = Iraq2018nomiss2) Iraq2018weighted <- rake(design = Iraq2018w, sample.margins = list(~weightcats), population.margins = list(Iraq18.distribution)) table(Iraq2018nomiss2$Surveytype) svytable(~Surveytype, Iraq2018weighted) #WORKED. table(Iraq2018nomiss2$gender) svytable(~gender, Iraq2018weighted) #worked. table(Iraq2018nomiss2$gender, Iraq2018nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Iraq2018weighted) #WORKED. ################################################################################################################## ################################################################################################################## #########################################ANALYSES IRAQ2018 WEIGHTED############################################## ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Iraq2018weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Iraq2018weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Iraq2018weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Iraq2018weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Iraq2018weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Iraq2018weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Iraq2018weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Iraq2018weighted, Surveytype ==1))) #TRUST mIraq2018weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted, family = quasibinomial) summary(mIraq2018weighted21b) mIraq2018weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted, family = quasibinomial) summary(mIraq2018weighted21c) mIraq2018weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted, family = quasibinomial) summary(mIraq2018weighted21c2) mIraq2018weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted, family = quasibinomial) summary(mIraq2018weighted21d) mIraq2018weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted, family = quasibinomial) summary(mIraq2018weighted21d2) mIraq2018weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted, family = quasibinomial) summary(mIraq2018weighted21e) mIraq2018weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted, family = quasibinomial) summary(mIraq2018weighted21e2) mIraq2018weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted, family = quasibinomial) summary(mIraq2018weighted21f) mIraq2018weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted, family = quasibinomial) summary(mIraq2018weighted21f2) #TRUST POL mIraq20182b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq2018nomiss) summary(mIraq20182b) mIraq20182b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq2018nomiss) summary(mIraq20182b2) mIraq2018weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted24c) mIraq2018weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted24c2) mIraq2018weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted24d) mIraq2018weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted24d2) mIraq2018weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted24e) mIraq2018weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted24e2) mIraq2018weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted24f) mIraq2018weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted24f2) #EDUC GE mIraq2018weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted22b) mIraq2018weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted22b2) mIraq2018weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted22c) mIraq2018weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted22c2) mIraq2018weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted22d) mIraq2018weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted22d2) mIraq2018weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted22e) mIraq2018weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted22e2) mIraq2018weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted22f) mIraq2018weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted22f2) #POL GE mIraq20182b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq2018nomiss) summary(mIraq20182b) mIraq20182b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq2018nomiss) summary(mIraq20182b2) mIraq2018weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted23c) mIraq2018weighted23c2 <- svyglm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted23c2) mIraq2018weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted23d) mIraq2018weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted23d2) mIraq2018weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted23e) mIraq2018weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted23e2) mIraq2018weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted23f) mIraq2018weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Iraq2018weighted) summary(mIraq2018weighted23f2) ################################################################################################################## ################################################################################################################## ###########################################CREATING WEIGHTS JORDAN 2013############################################ ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Jordan2018 <- ABWVS20132018[ABWVS20132018$country == "Jordan" & ABWVS20132018$year == 2018,] table(Jordan2018$country) table(Jordan2018$year) Jordan2018$weightcats <- 99999 table(Jordan2018$weightcats) Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 1 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 2 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 3 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 4 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 5 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 6 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 7 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 8 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 9 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 10 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 11 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 12 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 13 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 14 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 15 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 16 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 17 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 18 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 19 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 20 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 21 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 22 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 23 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 0 ] <- 24 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 25 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 26 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 27 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 28 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 29 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 30 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 31 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 32 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 33 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 34 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 35 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 36 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 37 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 38 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 39 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 40 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 41 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 42 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 43 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 44 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 45 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 46 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 47 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 0 ] <- 48 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 49 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 50 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 51 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 52 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 53 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 54 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 55 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 56 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 57 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 58 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 59 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 60 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 61 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 62 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 63 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 64 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 65 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 66 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 67 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 68 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 69 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 70 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 71 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==0 & Jordan2018$Surveytype == 1 ] <- 72 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 73 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 74 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 75 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 76 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 77 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 78 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 79 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 80 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 81 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 82 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 83 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==0 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 84 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 85 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 86 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 87 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 88 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 89 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==0 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 90 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 91 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==0 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 92 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 93 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==1 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 94 Jordan2018$weightcats[Jordan2018$gender == 0 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 95 Jordan2018$weightcats[Jordan2018$gender == 1 & Jordan2018$agecats ==2 & Jordan2018$Educcats ==1 & Jordan2018$Mar2 ==1 & Jordan2018$Empl ==1 & Jordan2018$Surveytype == 1 ] <- 96 table(Jordan2018$weightcats) Jordan2018nomiss <- Jordan2018[Jordan2018$weightcats != 99999,] table(Jordan2018nomiss$weightcats) prop.table(table(Jordan2018nomiss$weightcats, Jordan2018nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 26 95 Jordan2018nomiss2 <- Jordan2018nomiss[Jordan2018nomiss$weightcats != 26 & Jordan2018nomiss$weightcats != 95,] table(Jordan2018nomiss2$weightcats) Jordan18.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", "73", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "96"), Freq = nrow(Jordan2018nomiss2) * c( 0.0005563282, 0.0105702364, 0.0091794159, 0.0436717663, 0.033379694, 0.0225312935, 0.0005563282, 0.0050069541, 0.0022253129, 0.0100139082, 0.0086230876, 0.0052851182, 0.0169680111, 0.0055632823, 0.0011126565, 0.0083449235, 0.0019471488, 0.0222531293, 0.0050069541, 0.0013908206, 0.0002781641, 0.0013908206, 0.0002781641, 0.0022253129, 0.0052851182, 0.0289290682, 0.0050069541, 0.0125173853, 0.0011126565, 0.0008344924, 0.0016689847, 0.0116828929, 0.0105702364, 0.00472879, 0.0016689847, 0.0125173853, 0.0011126565, 0.0044506259, 0.0011126565, 0.0002781641, 0.0008344924, 0.0044506259, 0.0005563282, 0.0030598053, 0.0025034771, 0.0002781641, 0.0005563282, 0.0105702364, 0.0091794159, 0.0436717663, 0.033379694, 0.0225312935, 0.0005563282, 0.0050069541, 0.0022253129, 0.0100139082, 0.0086230876, 0.0052851182, 0.0169680111, 0.0055632823, 0.0011126565, 0.0083449235, 0.0019471488, 0.0222531293, 0.0050069541, 0.0013908206, 0.0002781641, 0.0013908206, 0.0002781641, 0.0022253129, 0.0052851182, 0.0289290682, 0.0050069541, 0.0125173853, 0.0011126565, 0.0008344924, 0.0016689847, 0.0116828929, 0.0105702364, 0.00472879, 0.0016689847, 0.0125173853, 0.0011126565, 0.0044506259, 0.0011126565, 0.0002781641, 0.0008344924, 0.0044506259, 0.0005563282, 0.0030598053, 0.0025034771, 0.0002781641)) Jordan2018w <- svydesign(ids=~1, data = Jordan2018nomiss2) Jordan2018weighted <- rake(design = Jordan2018w, sample.margins = list(~weightcats), population.margins = list(Jordan18.distribution)) table(Jordan2018nomiss2$Surveytype) svytable(~Surveytype, Jordan2018weighted) #WORKED. table(Jordan2018nomiss2$gender) svytable(~gender, Jordan2018weighted) #worked. table(Jordan2018nomiss2$gender, Jordan2018nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Jordan2018weighted) #WORKED. ################################################################################################################## ################################################################################################################## #########################################ANALYSES JORDAN2018 WEIGHTED############################################# ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Jordan2018weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Jordan2018weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Jordan2018weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Jordan2018weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Jordan2018weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Jordan2018weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Jordan2018weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Jordan2018weighted, Surveytype ==1))) #TRUST mJordan2018weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted, family = quasibinomial) summary(mJordan2018weighted21b) mJordan2018weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted, family = quasibinomial) summary(mJordan2018weighted21c) mJordan2018weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted, family = quasibinomial) summary(mJordan2018weighted21c2) mJordan2018weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted, family = quasibinomial) summary(mJordan2018weighted21d) mJordan2018weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted, family = quasibinomial) summary(mJordan2018weighted21d2) mJordan2018weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted, family = quasibinomial) summary(mJordan2018weighted21e) mJordan2018weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted, family = quasibinomial) summary(mJordan2018weighted21e2) mJordan2018weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted, family = quasibinomial) summary(mJordan2018weighted21f) mJordan2018weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted, family = quasibinomial) summary(mJordan2018weighted21f2) #TRUST POL mJordan20182b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan2018nomiss) summary(mJordan20182b) mJordan20182b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Jordan2018nomiss) summary(mJordan20182b2) mJordan2018weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted24c) mJordan2018weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted24c2) mJordan2018weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted24d) mJordan2018weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted24d2) mJordan2018weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted24e) mJordan2018weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted24e2) mJordan2018weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted24f) mJordan2018weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted24f2) #EDUC GE mJordan2018weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted22b) mJordan2018weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted22b2) mJordan2018weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted22c) mJordan2018weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted22c2) mJordan2018weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted22d) mJordan2018weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted22d2) mJordan2018weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted22e) mJordan2018weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted22e2) mJordan2018weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted22f) mJordan2018weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted22f2) #POL GE mJordan20182b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan2018nomiss) summary(mJordan20182b) mJordan20182b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Jordan2018nomiss) summary(mJordan20182b2) mJordan2018weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted23c) mJordan2018weighted23c2 <- svyglm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted23c2) mJordan2018weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted23d) mJordan2018weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted23d2) mJordan2018weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted23e) mJordan2018weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted23e2) mJordan2018weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted23f) mJordan2018weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Jordan2018weighted) summary(mJordan2018weighted23f2) ################################################################################################################## ################################################################################################################## ###########################################CREATING WEIGHTS LEBANON 2013############################################ ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Lebanon2018 <- ABWVS20132018[ABWVS20132018$country == "Lebanon" & ABWVS20132018$year == 2018,] table(Lebanon2018$country) table(Lebanon2018$year) Lebanon2018$weightcats <- 99999 table(Lebanon2018$weightcats) Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 1 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 2 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 3 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 4 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 5 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 6 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 7 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 8 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 9 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 10 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 11 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 12 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 13 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 14 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 15 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 16 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 17 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 18 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 19 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 20 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 21 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 22 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 23 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 0 ] <- 24 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 25 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 26 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 27 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 28 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 29 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 30 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 31 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 32 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 33 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 34 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 35 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 36 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 37 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 38 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 39 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 40 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 41 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 42 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 43 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 44 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 45 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 46 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 47 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 0 ] <- 48 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 49 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 50 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 51 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 52 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 53 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 54 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 55 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 56 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 57 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 58 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 59 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 60 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 61 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 62 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 63 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 64 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 65 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 66 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 67 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 68 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 69 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 70 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 71 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==0 & Lebanon2018$Surveytype == 1 ] <- 72 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 73 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 74 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 75 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 76 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 77 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 78 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 79 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 80 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 81 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 82 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 83 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==0 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 84 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 85 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 86 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 87 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 88 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 89 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==0 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 90 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 91 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==0 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 92 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 93 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==1 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 94 Lebanon2018$weightcats[Lebanon2018$gender == 0 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 95 Lebanon2018$weightcats[Lebanon2018$gender == 1 & Lebanon2018$agecats ==2 & Lebanon2018$Educcats ==1 & Lebanon2018$Mar2 ==1 & Lebanon2018$Empl ==1 & Lebanon2018$Surveytype == 1 ] <- 96 table(Lebanon2018$weightcats) Lebanon2018nomiss <- Lebanon2018[Lebanon2018$weightcats != 99999,] table(Lebanon2018nomiss$weightcats) prop.table(table(Lebanon2018nomiss$weightcats, Lebanon2018nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 69 Lebanon2018nomiss2 <- Lebanon2018nomiss[Lebanon2018nomiss$weightcats != 69,] table(Lebanon2018nomiss2$weightcats) Lebanon18.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "96"), Freq = nrow(Lebanon2018nomiss2) * c( 0.0005560189, 0.0044481512, 0.0005560189, 0.0247428413, 0.0047261607, 0.0161245482, 0.0002780095, 0.0016680567, 0.0002780095, 0.0066722269, 0.0022240756, 0.003058104, 0.0069502363, 0.0094523214, 0.0019460662, 0.0019460662, 0.0019460662, 0.0058381985, 0.0083402836, 0.0111203781, 0.0005560189, 0.0005560189, 0.0013900473, 0.0038921323, 0.0016680567, 0.031971087, 0.0139004726, 0.0308590492, 0.0066722269, 0.0013900473, 0.0038921323, 0.0150125104, 0.0130664443, 0.0088963025, 0.0033361134, 0.0161245482, 0.0058381985, 0.008618293, 0.0033361134, 0.0027800945, 0.006116208, 0.0127884348, 0.0125104254, 0.0052821796, 0.0066722269, 0.0008340284, 0.0027800945, 0.0005560189, 0.0044481512, 0.0005560189, 0.0247428413, 0.0047261607, 0.0161245482, 0.0002780095, 0.0016680567, 0.0002780095, 0.0066722269, 0.0022240756, 0.003058104, 0.0069502363, 0.0094523214, 0.0019460662, 0.0019460662, 0.0019460662, 0.0058381985, 0.0083402836, 0.0111203781, 0.0005560189, 0.0005560189, 0.0013900473, 0.0038921323, 0.0016680567, 0.031971087, 0.0139004726, 0.0308590492, 0.0066722269, 0.0013900473, 0.0038921323, 0.0150125104, 0.0130664443, 0.0088963025, 0.0033361134, 0.0161245482, 0.0058381985, 0.008618293, 0.0033361134, 0.0027800945, 0.006116208, 0.0127884348, 0.0125104254, 0.0052821796, 0.0066722269, 0.0008340284, 0.0027800945)) Lebanon2018w <- svydesign(ids=~1, data = Lebanon2018nomiss2) Lebanon2018weighted <- rake(design = Lebanon2018w, sample.margins = list(~weightcats), population.margins = list(Lebanon18.distribution)) table(Lebanon2018nomiss2$Surveytype) svytable(~Surveytype, Lebanon2018weighted) #WORKED. table(Lebanon2018nomiss2$gender) svytable(~gender, Lebanon2018weighted) #worked. table(Lebanon2018nomiss2$gender, Lebanon2018nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Lebanon2018weighted) #WORKED. ################################################################################################################## ################################################################################################################## #########################################ANALYSES LEBANON2018 WEIGHTED############################################# ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Lebanon2018weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Lebanon2018weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Lebanon2018weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Lebanon2018weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Lebanon2018weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Lebanon2018weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Lebanon2018weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Lebanon2018weighted, Surveytype ==1))) #TRUST mLebanon2018weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted, family = quasibinomial) summary(mLebanon2018weighted21b) mLebanon2018weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted, family = quasibinomial) summary(mLebanon2018weighted21c) mLebanon2018weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted, family = quasibinomial) summary(mLebanon2018weighted21c2) mLebanon2018weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted, family = quasibinomial) summary(mLebanon2018weighted21d) mLebanon2018weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted, family = quasibinomial) summary(mLebanon2018weighted21d2) mLebanon2018weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted, family = quasibinomial) summary(mLebanon2018weighted21e) mLebanon2018weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted, family = quasibinomial) summary(mLebanon2018weighted21e2) mLebanon2018weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted, family = quasibinomial) summary(mLebanon2018weighted21f) mLebanon2018weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted, family = quasibinomial) summary(mLebanon2018weighted21f2) #TRUST POL mLebanon20182b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon2018nomiss) summary(mLebanon20182b) mLebanon20182b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon2018nomiss) summary(mLebanon20182b2) mLebanon2018weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted24c) mLebanon2018weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted24c2) mLebanon2018weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted24d) mLebanon2018weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted24d2) mLebanon2018weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted24e) mLebanon2018weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted24e2) mLebanon2018weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted24f) mLebanon2018weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted24f2) #EDUC GE mLebanon2018weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted22b) mLebanon2018weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted22b2) mLebanon2018weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted22c) mLebanon2018weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted22c2) mLebanon2018weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted22d) mLebanon2018weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted22d2) mLebanon2018weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted22e) mLebanon2018weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted22e2) mLebanon2018weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted22f) mLebanon2018weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted22f2) #POL GE mLebanon20182b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon2018nomiss) summary(mLebanon20182b) mLebanon20182b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon2018nomiss) summary(mLebanon20182b2) mLebanon2018weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted23c) mLebanon2018weighted23c2 <- svyglm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted23c2) mLebanon2018weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted23d) mLebanon2018weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted23d2) mLebanon2018weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted23e) mLebanon2018weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted23e2) mLebanon2018weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted23f) mLebanon2018weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Lebanon2018weighted) summary(mLebanon2018weighted23f2) ################################################################################################################## ################################################################################################################## ###########################################CREATING WEIGHTS TUNISIA 2013############################################ ################################################################################################################## ################################################################################################################## table (ABWVS20132018$country) table (ABWVS20132018$year) Tunisia2018 <- ABWVS20132018[ABWVS20132018$country == "Tunisia" & ABWVS20132018$year == 2018,] table(Tunisia2018$country) table(Tunisia2018$year) Tunisia2018$weightcats <- 99999 table(Tunisia2018$weightcats) Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 1 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 2 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 3 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 4 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 5 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 6 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 7 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 8 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 9 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 10 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 11 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 12 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 13 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 14 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 15 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 16 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 17 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 18 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 19 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 20 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 21 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 22 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 23 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 0 ] <- 24 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 25 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 26 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 27 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 28 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 29 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 30 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 31 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 32 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 33 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 34 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 35 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 36 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 37 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 38 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 39 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 40 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 41 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 42 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 43 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 44 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 45 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 46 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 47 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 0 ] <- 48 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 49 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 50 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 51 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 52 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 53 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 54 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 55 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 56 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 57 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 58 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 59 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 60 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 61 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 62 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 63 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 64 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 65 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 66 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 67 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 68 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 69 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 70 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 71 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==0 & Tunisia2018$Surveytype == 1 ] <- 72 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 73 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 74 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 75 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 76 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 77 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 78 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 79 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 80 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 81 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 82 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 83 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==0 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 84 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 85 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 86 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 87 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 88 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 89 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==0 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 90 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 91 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==0 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 92 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 93 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==1 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 94 Tunisia2018$weightcats[Tunisia2018$gender == 0 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 95 Tunisia2018$weightcats[Tunisia2018$gender == 1 & Tunisia2018$agecats ==2 & Tunisia2018$Educcats ==1 & Tunisia2018$Mar2 ==1 & Tunisia2018$Empl ==1 & Tunisia2018$Surveytype == 1 ] <- 96 table(Tunisia2018$weightcats) Tunisia2018nomiss <- Tunisia2018[Tunisia2018$weightcats != 99999,] table(Tunisia2018nomiss$weightcats) prop.table(table(Tunisia2018nomiss$weightcats, Tunisia2018nomiss$Surveytype)) #cats without respondents in their accompanying WVS/AB countercategory: 72 79 Tunisia2018nomiss2 <- Tunisia2018nomiss[Tunisia2018nomiss$weightcats != 72 & Tunisia2018nomiss$weightcats != 79,] table(Tunisia2018nomiss2$weightcats) Tunisia18.distribution <- data.frame(weightcats = c( "1", "2", "3", "4", "5", "6", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "25", "26", "27", "28", "29", "30", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "48", "49", "50", "51", "52", "53", "54", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "73", "74", "75", "76", "77", "78", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "96"), Freq = nrow(Tunisia2018nomiss2) * c( 0.0014156285, 0.0053793884, 0.0036806342, 0.0339750849, 0.0178369196, 0.0294450736, 0.0011325028, 0.0005662514, 0.0036806342, 0.0016987542, 0.0002831257, 0.011608154, 0.0127406569, 0.0039637599, 0.0056625142, 0.0016987542, 0.0181200453, 0.0016987542, 0.0082106455, 0.0014156285, 0.0005662514, 0.0005662514, 0.0028312571, 0.0031143828, 0.0305775764, 0.0144394111, 0.025198188, 0.0090600227, 0.0008493771, 0.0082106455, 0.0045300113, 0.0014156285, 0.0008493771, 0.0172706682, 0.0059456399, 0.0144394111, 0.0076443941, 0.0016987542, 0.0042468856, 0.0031143828, 0.00198188, 0.0025481314, 0.0014156285, 0.0002831257, 0.0014156285, 0.0053793884, 0.0036806342, 0.0339750849, 0.0178369196, 0.0294450736, 0.0011325028, 0.0005662514, 0.0036806342, 0.0016987542, 0.0002831257, 0.011608154, 0.0127406569, 0.0039637599, 0.0056625142, 0.0016987542, 0.0181200453, 0.0016987542, 0.0082106455, 0.0014156285, 0.0005662514, 0.0005662514, 0.0028312571, 0.0031143828, 0.0305775764, 0.0144394111, 0.025198188, 0.0090600227, 0.0008493771, 0.0082106455, 0.0045300113, 0.0014156285, 0.0008493771, 0.0172706682, 0.0059456399, 0.0144394111, 0.0076443941, 0.0016987542, 0.0042468856, 0.0031143828, 0.00198188, 0.0025481314, 0.0014156285, 0.0002831257)) Tunisia2018w <- svydesign(ids=~1, data = Tunisia2018nomiss2) Tunisia2018weighted <- rake(design = Tunisia2018w, sample.margins = list(~weightcats), population.margins = list(Tunisia18.distribution)) table(Tunisia2018nomiss2$Surveytype) svytable(~Surveytype, Tunisia2018weighted) #WORKED. table(Tunisia2018nomiss2$gender) svytable(~gender, Tunisia2018weighted) #worked. table(Tunisia2018nomiss2$gender, Tunisia2018nomiss2$Surveytype) svytable(~interaction(gender, Surveytype), design = Tunisia2018weighted) #WORKED. ################################################################################################################## ################################################################################################################## #########################################ANALYSES TUNISIA2018 WEIGHTED############################################# ################################################################################################################## ################################################################################################################## prop.table(svytable(~Trust, subset(Tunisia2018weighted, Surveytype ==0))) prop.table(svytable(~Trust, subset(Tunisia2018weighted, Surveytype ==1))) prop.table(svytable(~Trustpol, subset(Tunisia2018weighted, Surveytype ==0))) prop.table(svytable(~Trustpol, subset(Tunisia2018weighted, Surveytype ==1))) prop.table(svytable(~Unieduc, subset(Tunisia2018weighted, Surveytype ==0))) prop.table(svytable(~Unieduc, subset(Tunisia2018weighted, Surveytype ==1))) prop.table(svytable(~PolLead, subset(Tunisia2018weighted, Surveytype ==0))) prop.table(svytable(~PolLead, subset(Tunisia2018weighted, Surveytype ==1))) #TRUST mTunisia2018weighted21b <- svyglm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted, family = quasibinomial) summary(mTunisia2018weighted21b) mTunisia2018weighted21c <- svyglm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted, family = quasibinomial) summary(mTunisia2018weighted21c) mTunisia2018weighted21c2 <- svyglm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted, family = quasibinomial) summary(mTunisia2018weighted21c2) mTunisia2018weighted21d <- svyglm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted, family = quasibinomial) summary(mTunisia2018weighted21d) mTunisia2018weighted21d2 <- svyglm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted, family = quasibinomial) summary(mTunisia2018weighted21d2) mTunisia2018weighted21e <- svyglm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted, family = quasibinomial) summary(mTunisia2018weighted21e) mTunisia2018weighted21e2 <- svyglm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted, family = quasibinomial) summary(mTunisia2018weighted21e2) mTunisia2018weighted21f <- svyglm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted, family = quasibinomial) summary(mTunisia2018weighted21f) mTunisia2018weighted21f2 <- svyglm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted, family = quasibinomial) summary(mTunisia2018weighted21f2) #TRUST POL mTunisia20182b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia2018nomiss) summary(mTunisia20182b) mTunisia20182b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia2018nomiss) summary(mTunisia20182b2) mTunisia2018weighted24c <- svyglm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted24c) mTunisia2018weighted24c2 <- svyglm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted24c2) mTunisia2018weighted24d <- svyglm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted24d) mTunisia2018weighted24d2 <- svyglm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted24d2) mTunisia2018weighted24e <- svyglm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted24e) mTunisia2018weighted24e2 <- svyglm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted24e2) mTunisia2018weighted24f <- svyglm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted24f) mTunisia2018weighted24f2 <- svyglm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted24f2) #EDUC GE mTunisia2018weighted22b <- svyglm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted22b) mTunisia2018weighted22b2 <- svyglm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted22b2) mTunisia2018weighted22c <- svyglm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted22c) mTunisia2018weighted22c2 <- svyglm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted22c2) mTunisia2018weighted22d <- svyglm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted22d) mTunisia2018weighted22d2 <- svyglm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted22d2) mTunisia2018weighted22e <- svyglm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted22e) mTunisia2018weighted22e2 <- svyglm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted22e2) mTunisia2018weighted22f <- svyglm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted22f) mTunisia2018weighted22f2 <- svyglm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted22f2) #POL GE mTunisia20182b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia2018nomiss) summary(mTunisia20182b) mTunisia20182b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia2018nomiss) summary(mTunisia20182b2) mTunisia2018weighted23c <- svyglm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted23c) mTunisia2018weighted23c2 <- svyglm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted23c2) mTunisia2018weighted23d <- svyglm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted23d) mTunisia2018weighted23d2 <- svyglm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted23d2) mTunisia2018weighted23e <- svyglm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted23e) mTunisia2018weighted23e2 <- svyglm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted23e2) mTunisia2018weighted23f <- svyglm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted23f) mTunisia2018weighted23f2 <- svyglm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, design = Tunisia2018weighted) summary(mTunisia2018weighted23f2) ################################################################################################################## ################################################################################################################## #########################################COMPARING DEMOGRAPHIC DISTRIBUTIONS###################################### ################################################################################################################## ################################################################################################################## #########################################DEMOGRAPHICS OVER ALL COUNTRIES########################################## #gender. prop.table(table(ABWVS20132018$gender, ABWVS20132018$Surveytype),2) chisq.test(table(ABWVS20132018$gender, ABWVS20132018$Surveytype)) #age. aggregate(ABWVS20132018$age_short ~ Surveytype, ABWVS20132018, min) aggregate(ABWVS20132018$age_short ~ Surveytype, ABWVS20132018, max) aggregate(ABWVS20132018$age_short ~ Surveytype, ABWVS20132018, mean) aggregate(ABWVS20132018$age_short ~ Surveytype, ABWVS20132018, sd) t.test(ABWVS20132018$age_short ~ ABWVS20132018$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(ABWVS20132018$Educ ~ Surveytype, ABWVS20132018, min) aggregate(ABWVS20132018$Educ ~ Surveytype, ABWVS20132018, max) aggregate(ABWVS20132018$Educ ~ Surveytype, ABWVS20132018, mean) aggregate(ABWVS20132018$Educ ~ Surveytype, ABWVS20132018, sd) t.test(ABWVS20132018$Educ ~ ABWVS20132018$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(ABWVS20132018$Mar, ABWVS20132018$Surveytype),2) chisq.test(table(ABWVS20132018$Mar, ABWVS20132018$Surveytype)) #employment status. prop.table(table(ABWVS20132018$Empl, ABWVS20132018$Surveytype),2) chisq.test(table(ABWVS20132018$Empl, ABWVS20132018$Surveytype)) #########################################DEMOGRAPHICS PER SURVEY################################################## ###############################################ALGERIA############################################################ table(ABWVS20132018$country) Algeria <- ABWVS20132018[ABWVS20132018$country == "Algeria",] table(Algeria$country) table(ABWVS20132018$country) table(Algeria$year) #gender. prop.table(table(Algeria$gender, Algeria$Surveytype),2) chisq.test(table(Algeria$gender, Algeria$Surveytype)) #age. aggregate(Algeria $age_short ~ Surveytype, Algeria, min) aggregate(Algeria $age_short ~ Surveytype, Algeria, max) aggregate(Algeria$age_short ~ Surveytype, Algeria, mean) aggregate(Algeria$age_short ~ Surveytype, Algeria, sd) t.test(Algeria$age_short ~ Algeria$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Algeria$Educ ~ Surveytype, Algeria, min) aggregate(Algeria$Educ ~ Surveytype, Algeria, max) aggregate(Algeria$Educ ~ Surveytype, Algeria, mean) aggregate(Algeria$Educ ~ Surveytype, Algeria, sd) t.test(Algeria$Educ ~ Algeria$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Algeria$Mar, Algeria$Surveytype),2) chisq.test(table(Algeria$Mar, Algeria$Surveytype)) #employment status. prop.table(table(Algeria$Empl, Algeria$Surveytype),2) chisq.test(table(Algeria$Empl, Algeria$Surveytype)) #weights table(Algeria$Surveytype) prop.table(table(Algeria$Surveytype, Algeria$weightcats),1) ###############################################EGYPT13############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Egypt13 <- ABWVS20132018[ABWVS20132018$country == "Egypt" & ABWVS20132018$year == 2013, ] table(Egypt13$country) table(Egypt13$year) #gender. prop.table(table(Egypt13$gender, Egypt13$Surveytype),2) chisq.test(table(Egypt13$gender, Egypt13$Surveytype)) #age. aggregate(Egypt13 $age_short ~ Surveytype, Egypt13, min) aggregate(Egypt13 $age_short ~ Surveytype, Egypt13, max) aggregate(Egypt13$age_short ~ Surveytype, Egypt13, mean) aggregate(Egypt13$age_short ~ Surveytype, Egypt13, sd) t.test(Egypt13$age_short ~ Egypt13$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Egypt13$Educ ~ Surveytype, Egypt13, min) aggregate(Egypt13$Educ ~ Surveytype, Egypt13, max) aggregate(Egypt13$Educ ~ Surveytype, Egypt13, mean) aggregate(Egypt13$Educ ~ Surveytype, Egypt13, sd) t.test(Egypt13$Educ ~ Egypt13$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Egypt13$Mar, Egypt13$Surveytype),2) chisq.test(table(Egypt13$Mar, Egypt13$Surveytype)) #employment status. prop.table(table(Egypt13$Empl, Egypt13$Surveytype),2) chisq.test(table(Egypt13$Empl, Egypt13$Surveytype)) #weights table(Egypt13$Surveytype) prop.table(table(Egypt13$Surveytype, Egypt13$weightcats),1) ###############################################IRAQ13############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Iraq13 <- ABWVS20132018[ABWVS20132018$country == "Iraq" & ABWVS20132018$year == 2013, ] table(Iraq13$country) table(Iraq13$year) #gender. prop.table(table(Iraq13$gender, Iraq13$Surveytype),2) chisq.test(table(Iraq13$gender, Iraq13$Surveytype)) #age. aggregate(Iraq13 $age_short ~ Surveytype, Iraq13, min) aggregate(Iraq13 $age_short ~ Surveytype, Iraq13, max) aggregate(Iraq13$age_short ~ Surveytype, Iraq13, mean) aggregate(Iraq13$age_short ~ Surveytype, Iraq13, sd) t.test(Iraq13$age_short ~ Iraq13$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Iraq13$Educ ~ Surveytype, Iraq13, min) aggregate(Iraq13$Educ ~ Surveytype, Iraq13, max) aggregate(Iraq13$Educ ~ Surveytype, Iraq13, mean) aggregate(Iraq13$Educ ~ Surveytype, Iraq13, sd) t.test(Iraq13$Educ ~ Iraq13$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Iraq13$Mar, Iraq13$Surveytype),2) chisq.test(table(Iraq13$Mar, Iraq13$Surveytype)) #employment status. prop.table(table(Iraq13$Empl, Iraq13$Surveytype),2) chisq.test(table(Iraq13$Empl, Iraq13$Surveytype)) #weights table(Iraq13$Surveytype) prop.table(table(Iraq13$Surveytype, Iraq13$weightcats),1) ###############################################JORDAN13############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Jordan13 <- ABWVS20132018[ABWVS20132018$country == "Jordan" & ABWVS20132018$year == 2013, ] table(Jordan13$country) table(Jordan13$year) #gender. prop.table(table(Jordan13$gender, Jordan13$Surveytype),2) chisq.test(table(Jordan13$gender, Jordan13$Surveytype)) #age. aggregate(Jordan13 $age_short ~ Surveytype, Jordan13, min) aggregate(Jordan13 $age_short ~ Surveytype, Jordan13, max) aggregate(Jordan13$age_short ~ Surveytype, Jordan13, mean) aggregate(Jordan13$age_short ~ Surveytype, Jordan13, sd) t.test(Jordan13$age_short ~ Jordan13$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Jordan13$Educ ~ Surveytype, Jordan13, min) aggregate(Jordan13$Educ ~ Surveytype, Jordan13, max) aggregate(Jordan13$Educ ~ Surveytype, Jordan13, mean) aggregate(Jordan13$Educ ~ Surveytype, Jordan13, sd) t.test(Jordan13$Educ ~ Jordan13$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Jordan13$Mar, Jordan13$Surveytype),2) chisq.test(table(Jordan13$Mar, Jordan13$Surveytype)) #employment status. prop.table(table(Jordan13$Empl, Jordan13$Surveytype),2) chisq.test(table(Jordan13$Empl, Jordan13$Surveytype)) #weights table(Jordan13$Surveytype) prop.table(table(Jordan13$Surveytype, Jordan13$weightcats),1) ###############################################KUWAIT############################################################ table(ABWVS20132018$country) Kuwait <- ABWVS20132018[ABWVS20132018$country == "Kuwait", ] table(Kuwait$country) table(Kuwait$year) #gender. prop.table(table(Kuwait$gender, Kuwait$Surveytype),2) chisq.test(table(Kuwait$gender, Kuwait$Surveytype)) #age. aggregate(Kuwait $age_short ~ Surveytype, Kuwait, min) aggregate(Kuwait $age_short ~ Surveytype, Kuwait, max) aggregate(Kuwait$age_short ~ Surveytype, Kuwait, mean) aggregate(Kuwait$age_short ~ Surveytype, Kuwait, sd) t.test(Kuwait$age_short ~ Kuwait$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Kuwait$Educ ~ Surveytype, Kuwait, min) aggregate(Kuwait$Educ ~ Surveytype, Kuwait, max) aggregate(Kuwait$Educ ~ Surveytype, Kuwait, mean) aggregate(Kuwait$Educ ~ Surveytype, Kuwait, sd) t.test(Kuwait$Educ ~ Kuwait$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Kuwait$Mar, Kuwait$Surveytype),2) chisq.test(table(Kuwait$Mar, Kuwait$Surveytype)) #employment status. prop.table(table(Kuwait$Empl, Kuwait$Surveytype),2) chisq.test(table(Kuwait$Empl, Kuwait$Surveytype)) #weights table(Kuwait$Surveytype) prop.table(table(Kuwait$Surveytype, Kuwait$weightcats),1) ###############################################LEBANON13############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Lebanon13 <- ABWVS20132018[ABWVS20132018$country == "Lebanon" & ABWVS20132018$year == 2013, ] table(Lebanon13$country) table(Lebanon13$year) #gender. prop.table(table(Lebanon13$gender, Lebanon13$Surveytype),2) chisq.test(table(Lebanon13$gender, Lebanon13$Surveytype)) #age. aggregate(Lebanon13 $age_short ~ Surveytype, Lebanon13, min) aggregate(Lebanon13 $age_short ~ Surveytype, Lebanon13, max) aggregate(Lebanon13$age_short ~ Surveytype, Lebanon13, mean) aggregate(Lebanon13$age_short ~ Surveytype, Lebanon13, sd) t.test(Lebanon13$age_short ~ Lebanon13$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Lebanon13$Educ ~ Surveytype, Lebanon13, min) aggregate(Lebanon13$Educ ~ Surveytype, Lebanon13, max) aggregate(Lebanon13$Educ ~ Surveytype, Lebanon13, mean) aggregate(Lebanon13$Educ ~ Surveytype, Lebanon13, sd) t.test(Lebanon13$Educ ~ Lebanon13$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Lebanon13$Mar, Lebanon13$Surveytype),2) chisq.test(table(Lebanon13$Mar, Lebanon13$Surveytype)) #employment status. prop.table(table(Lebanon13$Empl, Lebanon13$Surveytype),2) chisq.test(table(Lebanon13$Empl, Lebanon13$Surveytype)) #weights table(Lebanon13$Surveytype) prop.table(table(Lebanon13$Surveytype, Lebanon13$weightcats),1) ###############################################LIBYA############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Libya <- ABWVS20132018[ABWVS20132018$country == "Libya", ] table(Libya$country) table(Libya$year) #gender. prop.table(table(Libya$gender, Libya$Surveytype),2) chisq.test(table(Libya$gender, Libya$Surveytype)) #age. aggregate(Libya $age_short ~ Surveytype, Libya, min) aggregate(Libya $age_short ~ Surveytype, Libya, max) aggregate(Libya$age_short ~ Surveytype, Libya, mean) aggregate(Libya$age_short ~ Surveytype, Libya, sd) t.test(Libya$age_short ~ Libya$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Libya$Educ ~ Surveytype, Libya, min) aggregate(Libya$Educ ~ Surveytype, Libya, max) aggregate(Libya$Educ ~ Surveytype, Libya, mean) aggregate(Libya$Educ ~ Surveytype, Libya, sd) t.test(Libya$Educ ~ Libya$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Libya$Mar, Libya$Surveytype),2) chisq.test(table(Libya$Mar, Libya$Surveytype)) #employment status. prop.table(table(Libya$Empl, Libya$Surveytype),2) chisq.test(table(Libya$Empl, Libya$Surveytype)) #weights table(Libya$Surveytype) prop.table(table(Libya$Surveytype, Libya$weightcats),1) ###############################################MOROCCO############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Morocco <- ABWVS20132018[ABWVS20132018$country == "Morocco", ] table(Morocco$country) table(Morocco$year) #gender. prop.table(table(Morocco$gender, Morocco$Surveytype),2) chisq.test(table(Morocco$gender, Morocco$Surveytype)) #age. aggregate(Morocco $age_short ~ Surveytype, Morocco, min) aggregate(Morocco $age_short ~ Surveytype, Morocco, max) aggregate(Morocco$age_short ~ Surveytype, Morocco, mean) aggregate(Morocco$age_short ~ Surveytype, Morocco, sd) t.test(Morocco$age_short ~ Morocco$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Morocco$Educ ~ Surveytype, Morocco, min) aggregate(Morocco$Educ ~ Surveytype, Morocco, max) aggregate(Morocco$Educ ~ Surveytype, Morocco, mean) aggregate(Morocco$Educ ~ Surveytype, Morocco, sd) t.test(Morocco$Educ ~ Morocco$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Morocco$Mar, Morocco$Surveytype),2) chisq.test(table(Morocco$Mar, Morocco$Surveytype)) #employment status. prop.table(table(Morocco$Empl, Morocco$Surveytype),2) chisq.test(table(Morocco$Empl, Morocco$Surveytype)) #weights table(Morocco$Surveytype) prop.table(table(Morocco$Surveytype, Morocco$weightcats),1) ###############################################PALESTINE############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Palestine <- ABWVS20132018[ABWVS20132018$country == "Palestine", ] table(Palestine$country) table(Palestine$year) #gender. prop.table(table(Palestine$gender, Palestine$Surveytype),2) chisq.test(table(Palestine$gender, Palestine$Surveytype)) #age. aggregate(Palestine $age_short ~ Surveytype, Palestine, min) aggregate(Palestine $age_short ~ Surveytype, Palestine, max) aggregate(Palestine$age_short ~ Surveytype, Palestine, mean) aggregate(Palestine$age_short ~ Surveytype, Palestine, sd) t.test(Palestine$age_short ~ Palestine$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Palestine$Educ ~ Surveytype, Palestine, min) aggregate(Palestine$Educ ~ Surveytype, Palestine, max) aggregate(Palestine$Educ ~ Surveytype, Palestine, mean) aggregate(Palestine$Educ ~ Surveytype, Palestine, sd) t.test(Palestine$Educ ~ Palestine$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Palestine$Mar, Palestine$Surveytype),2) chisq.test(table(Palestine$Mar, Palestine$Surveytype)) #employment status. prop.table(table(Palestine$Empl, Palestine$Surveytype),2) chisq.test(table(Palestine$Empl, Palestine$Surveytype)) #weights table(Palestine$Surveytype) prop.table(table(Palestine$Surveytype, Palestine$weightcats),1) ###############################################TUNISIA13############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Tunisia13 <- ABWVS20132018[ABWVS20132018$country == "Tunisia" & ABWVS20132018$year == 2013, ] table(Tunisia13$country) table(Tunisia13$year) #gender. prop.table(table(Tunisia13$gender, Tunisia13$Surveytype),2) chisq.test(table(Tunisia13$gender, Tunisia13$Surveytype)) #age. aggregate(Tunisia13 $age_short ~ Surveytype, Tunisia13, min) aggregate(Tunisia13 $age_short ~ Surveytype, Tunisia13, max) aggregate(Tunisia13$age_short ~ Surveytype, Tunisia13, mean) aggregate(Tunisia13$age_short ~ Surveytype, Tunisia13, sd) t.test(Tunisia13$age_short ~ Tunisia13$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Tunisia13$Educ ~ Surveytype, Tunisia13, min) aggregate(Tunisia13$Educ ~ Surveytype, Tunisia13, max) aggregate(Tunisia13$Educ ~ Surveytype, Tunisia13, mean) aggregate(Tunisia13$Educ ~ Surveytype, Tunisia13, sd) t.test(Tunisia13$Educ ~ Tunisia13$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Tunisia13$Mar, Tunisia13$Surveytype),2) chisq.test(table(Tunisia13$Mar, Tunisia13$Surveytype)) #employment status. prop.table(table(Tunisia13$Empl, Tunisia13$Surveytype),2) chisq.test(table(Tunisia13$Empl, Tunisia13$Surveytype)) #weights table(Tunisia13$Surveytype) prop.table(table(Tunisia13$Surveytype, Tunisia13$weightcats),1) ###############################################YEMEN############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Yemen <- ABWVS20132018[ABWVS20132018$country == "Yemen", ] table(Yemen$country) table(Yemen$year) #gender. prop.table(table(Yemen$gender, Yemen$Surveytype),2) chisq.test(table(Yemen$gender, Yemen$Surveytype)) #age. aggregate(Yemen $age_short ~ Surveytype, Yemen, min) aggregate(Yemen $age_short ~ Surveytype, Yemen, max) aggregate(Yemen$age_short ~ Surveytype, Yemen, mean) aggregate(Yemen$age_short ~ Surveytype, Yemen, sd) t.test(Yemen$age_short ~ Yemen$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Yemen$Educ ~ Surveytype, Yemen, min) aggregate(Yemen$Educ ~ Surveytype, Yemen, max) aggregate(Yemen$Educ ~ Surveytype, Yemen, mean) aggregate(Yemen$Educ ~ Surveytype, Yemen, sd) t.test(Yemen$Educ ~ Yemen$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Yemen$Mar, Yemen$Surveytype),2) chisq.test(table(Yemen$Mar, Yemen$Surveytype)) #employment status. prop.table(table(Yemen$Empl, Yemen$Surveytype),2) chisq.test(table(Yemen$Empl, Yemen$Surveytype)) #weights table(Yemen$Surveytype) prop.table(table(Yemen$Surveytype, Yemen$weightcats),1) ###############################################EGYPT18############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Egypt18 <- ABWVS20132018[ABWVS20132018$country == "Egypt" & ABWVS20132018$year == 2018, ] table(Egypt18$country) table(Egypt18$year) #gender. prop.table(table(Egypt18$gender, Egypt18$Surveytype),2) chisq.test(table(Egypt18$gender, Egypt18$Surveytype)) #age. aggregate(Egypt18 $age_short ~ Surveytype, Egypt18, min) aggregate(Egypt18 $age_short ~ Surveytype, Egypt18, max) aggregate(Egypt18$age_short ~ Surveytype, Egypt18, mean) aggregate(Egypt18$age_short ~ Surveytype, Egypt18, sd) t.test(Egypt18$age_short ~ Egypt18$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Egypt18$Educ ~ Surveytype, Egypt18, min) aggregate(Egypt18$Educ ~ Surveytype, Egypt18, max) aggregate(Egypt18$Educ ~ Surveytype, Egypt18, mean) aggregate(Egypt18$Educ ~ Surveytype, Egypt18, sd) t.test(Egypt18$Educ ~ Egypt18$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Egypt18$Mar, Egypt18$Surveytype),2) chisq.test(table(Egypt18$Mar, Egypt18$Surveytype)) #employment status. prop.table(table(Egypt18$Empl, Egypt18$Surveytype),2) chisq.test(table(Egypt18$Empl, Egypt18$Surveytype)) #weights table(Egypt18$Surveytype) prop.table(table(Egypt18$Surveytype, Egypt18$weightcats),1) ###############################################IRAQ18############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Iraq18 <- ABWVS20132018[ABWVS20132018$country == "Iraq" & ABWVS20132018$year == 2018, ] table(Iraq18$country) table(Iraq18$year) #gender. prop.table(table(Iraq18$gender, Iraq18$Surveytype),2) chisq.test(table(Iraq18$gender, Iraq18$Surveytype)) #age. aggregate(Iraq18 $age_short ~ Surveytype, Iraq18, min) aggregate(Iraq18 $age_short ~ Surveytype, Iraq18, max) aggregate(Iraq18$age_short ~ Surveytype, Iraq18, mean) aggregate(Iraq18$age_short ~ Surveytype, Iraq18, sd) t.test(Iraq18$age_short ~ Iraq18$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Iraq18$Educ ~ Surveytype, Iraq18, min) aggregate(Iraq18$Educ ~ Surveytype, Iraq18, max) aggregate(Iraq18$Educ ~ Surveytype, Iraq18, mean) aggregate(Iraq18$Educ ~ Surveytype, Iraq18, sd) t.test(Iraq18$Educ ~ Iraq18$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Iraq18$Mar, Iraq18$Surveytype),2) chisq.test(table(Iraq18$Mar, Iraq18$Surveytype)) #employment status. prop.table(table(Iraq18$Empl, Iraq18$Surveytype),2) chisq.test(table(Iraq18$Empl, Iraq18$Surveytype)) #weights table(Iraq18$Surveytype) prop.table(table(Iraq18$Surveytype, Iraq18$weightcats),1) ###############################################JORDAN18############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Jordan18 <- ABWVS20132018[ABWVS20132018$country == "Jordan" & ABWVS20132018$year == 2018, ] table(Jordan18$country) table(Jordan18$year) #gender. prop.table(table(Jordan18$gender, Jordan18$Surveytype),2) chisq.test(table(Jordan18$gender, Jordan18$Surveytype)) #age. aggregate(Jordan18 $age_short ~ Surveytype, Jordan18, min) aggregate(Jordan18 $age_short ~ Surveytype, Jordan18, max) aggregate(Jordan18$age_short ~ Surveytype, Jordan18, mean) aggregate(Jordan18$age_short ~ Surveytype, Jordan18, sd) t.test(Jordan18$age_short ~ Jordan18$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Jordan18$Educ ~ Surveytype, Jordan18, min) aggregate(Jordan18$Educ ~ Surveytype, Jordan18, max) aggregate(Jordan18$Educ ~ Surveytype, Jordan18, mean) aggregate(Jordan18$Educ ~ Surveytype, Jordan18, sd) t.test(Jordan18$Educ ~ Jordan18$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Jordan18$Mar, Jordan18$Surveytype),2) chisq.test(table(Jordan18$Mar, Jordan18$Surveytype)) #employment status. prop.table(table(Jordan18$Empl, Jordan18$Surveytype),2) chisq.test(table(Jordan18$Empl, Jordan18$Surveytype)) #weights table(Jordan18$Surveytype) prop.table(table(Jordan18$Surveytype, Jordan18$weightcats),1) ###############################################LEBANON18############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Lebanon18 <- ABWVS20132018[ABWVS20132018$country == "Lebanon" & ABWVS20132018$year == 2018, ] table(Lebanon18$country) table(Lebanon18$year) #gender. prop.table(table(Lebanon18$gender, Lebanon18$Surveytype),2) chisq.test(table(Lebanon18$gender, Lebanon18$Surveytype)) #age. aggregate(Lebanon18 $age_short ~ Surveytype, Lebanon18, min) aggregate(Lebanon18 $age_short ~ Surveytype, Lebanon18, max) aggregate(Lebanon18$age_short ~ Surveytype, Lebanon18, mean) aggregate(Lebanon18$age_short ~ Surveytype, Lebanon18, sd) t.test(Lebanon18$age_short ~ Lebanon18$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Lebanon18$Educ ~ Surveytype, Lebanon18, min) aggregate(Lebanon18$Educ ~ Surveytype, Lebanon18, max) aggregate(Lebanon18$Educ ~ Surveytype, Lebanon18, mean) aggregate(Lebanon18$Educ ~ Surveytype, Lebanon18, sd) t.test(Lebanon18$Educ ~ Lebanon18$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Lebanon18$Mar, Lebanon18$Surveytype),2) chisq.test(table(Lebanon18$Mar, Lebanon18$Surveytype)) #employment status. prop.table(table(Lebanon18$Empl, Lebanon18$Surveytype),2) chisq.test(table(Lebanon18$Empl, Lebanon18$Surveytype)) #weights table(Lebanon18$Surveytype) prop.table(table(Lebanon18$Surveytype, Lebanon18$weightcats),1) ###############################################TUNISIA18############################################################ table(ABWVS20132018$country) table(ABWVS20132018$year) Tunisia18 <- ABWVS20132018[ABWVS20132018$country == "Tunisia" & ABWVS20132018$year == 2018, ] table(Tunisia18$country) table(Tunisia18$year) #gender. prop.table(table(Tunisia18$gender, Tunisia18$Surveytype),2) chisq.test(table(Tunisia18$gender, Tunisia18$Surveytype)) #age. aggregate(Tunisia18 $age_short ~ Surveytype, Tunisia18, min) aggregate(Tunisia18 $age_short ~ Surveytype, Tunisia18, max) aggregate(Tunisia18$age_short ~ Surveytype, Tunisia18, mean) aggregate(Tunisia18$age_short ~ Surveytype, Tunisia18, sd) t.test(Tunisia18$age_short ~ Tunisia18$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #education. aggregate(Tunisia18$Educ ~ Surveytype, Tunisia18, min) aggregate(Tunisia18$Educ ~ Surveytype, Tunisia18, max) aggregate(Tunisia18$Educ ~ Surveytype, Tunisia18, mean) aggregate(Tunisia18$Educ ~ Surveytype, Tunisia18, sd) t.test(Tunisia18$Educ ~ Tunisia18$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #marital status. prop.table(table(Tunisia18$Mar, Tunisia18$Surveytype),2) chisq.test(table(Tunisia18$Mar, Tunisia18$Surveytype)) #employment status. prop.table(table(Tunisia18$Empl, Tunisia18$Surveytype),2) chisq.test(table(Tunisia18$Empl, Tunisia18$Surveytype)) #weights table(Tunisia18$Surveytype) prop.table(table(Tunisia18$Surveytype, Tunisia18$weightcats),1) ################################################################################################################## ################################################################################################################## #########################################ANALYSIS 1: OVERALL DESCRIPTIVES######################################### ################################################################################################################## ################################################################################################################## summary(ABWVS20132018$Surveytype) aggregate(ABWVS20132018$Trust ~ Surveytype, ABWVS20132018, mean) chisq.test(table(ABWVS20132018$Trust,ABWVS20132018$Surveytype)) t.test(ABWVS20132018$Unieduc ~ ABWVS20132018$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(ABWVS20132018$PolLead ~ ABWVS20132018$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(ABWVS20132018$Trustpol ~ ABWVS20132018$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(ABWVS20132018$Trust, ABWVS20132018$Surveytype), 2) prop.table (table(ABWVS20132018$Unieduc, ABWVS20132018$Surveytype), 2) prop.table (table(ABWVS20132018$PolLead, ABWVS20132018$Surveytype), 2) prop.table (table(ABWVS20132018$Trustpol, ABWVS20132018$Surveytype), 2) aggregate(ABWVS20132018$Trust ~ Surveytype, ABWVS20132018, length) aggregate(ABWVS20132018$Unieduc ~ Surveytype, ABWVS20132018, length) aggregate(ABWVS20132018$PolLead ~ Surveytype, ABWVS20132018, length) aggregate(ABWVS20132018$Trustpol ~ Surveytype, ABWVS20132018, length) ################################################ ####################MEN######################### ################################################ table(ABWVS20132018$gender) Men <- ABWVS20132018[ABWVS20132018$gender == 0, ] table(Men$gender) table(ABWVS20132018$gender) summary(Men$Surveytype) aggregate(Men$Trust ~ Surveytype, Men, mean) chisq.test(table(Men$Trust,Men$Surveytype)) t.test(Men$Unieduc ~ Men$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Men$PolLead ~ Men$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Men$Trustpol ~ Men$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(Men$Trust, Men$Surveytype), 2) prop.table (table(Men$Unieduc, Men$Surveytype), 2) prop.table (table(Men$PolLead, Men$Surveytype), 2) prop.table (table(Men$Trustpol, Men$Surveytype), 2) aggregate(Men$Trust ~ Surveytype, Men, length) aggregate(Men$Unieduc ~ Surveytype, Men, length) aggregate(Men$PolLead ~ Surveytype, Men, length) aggregate(Men$Trustpol ~ Surveytype, Men, length) ################################################ ####################WOMEN####################### ################################################ table(ABWVS20132018$gender) Women <- ABWVS20132018[ABWVS20132018$gender == 1, ] table(Women$gender) table(ABWVS20132018$gender) summary(Women$Surveytype) aggregate(Women$Trust ~ Surveytype, Women, mean) chisq.test(table(Women$Trust,Women$Surveytype)) t.test(Women$Unieduc ~ Women$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Women$PolLead ~ Women$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Women$Trustpol ~ Women$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(Women$Trust, Women$Surveytype), 2) prop.table (table(Women$Unieduc, Women$Surveytype), 2) prop.table (table(Women$PolLead, Women$Surveytype), 2) prop.table (table(Women$Trustpol, Women$Surveytype), 2) aggregate(Women$Trust ~ Surveytype, Women, length) aggregate(Women$Unieduc ~ Surveytype, Women, length) aggregate(Women$PolLead ~ Surveytype, Women, length) aggregate(Women$Trustpol ~ Surveytype, Women, length) ################################################ ####################UNDER30##################### ################################################ table(ABWVS20132018$age_short) ABWVS20132018$agecats[ABWVS20132018$age_short < 30] <- 0 ABWVS20132018$agecats[ABWVS20132018$age_short > 29 & ABWVS20132018$age_short < 50] <-1 ABWVS20132018$agecats[ABWVS20132018$age_short > 49] <- 2 table(ABWVS20132018$agecats) table(ABWVS20132018$age_short, ABWVS20132018$agecats) table(ABWVS20132018$agecats) Under30 <- ABWVS20132018[ABWVS20132018$agecats == 0, ] table(Under30$age_short) table(ABWVS20132018$age_short) summary(Under30$Surveytype) aggregate(Under30$Trust ~ Surveytype, Under30, mean) chisq.test(table(Under30$Trust,Under30$Surveytype)) t.test(Under30$Unieduc ~ Under30$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Under30$PolLead ~ Under30$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Under30$Trustpol ~ Under30$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(Under30$Trust, Under30$Surveytype), 2) prop.table (table(Under30$Unieduc, Under30$Surveytype), 2) prop.table (table(Under30$PolLead, Under30$Surveytype), 2) prop.table (table(Under30$Trustpol, Under30$Surveytype), 2) aggregate(Under30$Trust ~ Surveytype, Under30, length) aggregate(Under30$Unieduc ~ Surveytype, Under30, length) aggregate(Under30$PolLead ~ Surveytype, Under30, length) aggregate(Under30$Trustpol ~ Surveytype, Under30, length) ################################################ #################From30to49##################### ################################################ table(ABWVS20132018$agecats) From30to49 <- ABWVS20132018[ABWVS20132018$agecats == 1, ] table(From30to49$age_short) table(ABWVS20132018$age_short) summary(From30to49$Surveytype) aggregate(From30to49$Trust ~ Surveytype, From30to49, mean) chisq.test(table(From30to49$Trust,From30to49$Surveytype)) t.test(From30to49$Unieduc ~ From30to49$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(From30to49$PolLead ~ From30to49$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(From30to49$Trustpol ~ From30to49$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(From30to49$Trust, From30to49$Surveytype), 2) prop.table (table(From30to49$Unieduc, From30to49$Surveytype), 2) prop.table (table(From30to49$PolLead, From30to49$Surveytype), 2) prop.table (table(From30to49$Trustpol, From30to49$Surveytype), 2) aggregate(From30to49$Trust ~ Surveytype, From30to49, length) aggregate(From30to49$Unieduc ~ Surveytype, From30to49, length) aggregate(From30to49$PolLead ~ Surveytype, From30to49, length) aggregate(From30to49$Trustpol ~ Surveytype, From30to49, length) ################################################ #################Over49######################### ################################################ table(ABWVS20132018$agecats) Over49 <- ABWVS20132018[ABWVS20132018$agecats == 2, ] table(Over49$age_short) table(ABWVS20132018$age_short) summary(Over49$Surveytype) aggregate(Over49$Trust ~ Surveytype, Over49, mean) chisq.test(table(Over49$Trust,Over49$Surveytype)) t.test(Over49$Unieduc ~ Over49$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Over49$PolLead ~ Over49$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Over49$Trustpol ~ Over49$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(Over49$Trust, Over49$Surveytype), 2) prop.table (table(Over49$Unieduc, Over49$Surveytype), 2) prop.table (table(Over49$PolLead, Over49$Surveytype), 2) prop.table (table(Over49$Trustpol, Over49$Surveytype), 2) aggregate(Over49$Trust ~ Surveytype, Over49, length) aggregate(Over49$Unieduc ~ Surveytype, Over49, length) aggregate(Over49$PolLead ~ Surveytype, Over49, length) aggregate(Over49$Trustpol ~ Surveytype, Over49, length) ################################################ #################LowerEducated################## ################################################ table(ABWVS20132018$Educ) # 0 - No, 1 - Primary, 2 - Secondary, 3 - Tertiary ABWVS20132018$Educcats[ABWVS20132018$Educ < 3] <- 0 ABWVS20132018$Educcats[ABWVS20132018$Educ == 3] <- 1 table(ABWVS20132018$Educcats) LowerEducated <- ABWVS20132018[ABWVS20132018$Educcats == 0, ] table(LowerEducated$Educ) table(ABWVS20132018$Educ) summary(LowerEducated$Surveytype) aggregate(LowerEducated$Trust ~ Surveytype, LowerEducated, mean) chisq.test(table(LowerEducated$Trust,LowerEducated$Surveytype)) t.test(LowerEducated$Unieduc ~ LowerEducated$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(LowerEducated$PolLead ~ LowerEducated$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(LowerEducated$Trustpol ~ LowerEducated$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(LowerEducated$Trust, LowerEducated$Surveytype), 2) prop.table (table(LowerEducated$Unieduc, LowerEducated$Surveytype), 2) prop.table (table(LowerEducated$PolLead, LowerEducated$Surveytype), 2) prop.table (table(LowerEducated$Trustpol, LowerEducated$Surveytype), 2) aggregate(LowerEducated$Trust ~ Surveytype, LowerEducated, length) aggregate(LowerEducated$Unieduc ~ Surveytype, LowerEducated, length) aggregate(LowerEducated$PolLead ~ Surveytype, LowerEducated, length) aggregate(LowerEducated$Trustpol ~ Surveytype, LowerEducated, length) ################################################ #################HigherEducated################# ################################################ table(ABWVS20132018$Educcats) HigherEducated <- ABWVS20132018[ABWVS20132018$Educcats == 1, ] table(HigherEducated$Educ) table(ABWVS20132018$Educ) summary(HigherEducated$Surveytype) aggregate(HigherEducated$Trust ~ Surveytype, HigherEducated, mean) chisq.test(table(HigherEducated$Trust,HigherEducated$Surveytype)) t.test(HigherEducated$Unieduc ~ HigherEducated$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(HigherEducated$PolLead ~ HigherEducated$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(HigherEducated$Trustpol ~ HigherEducated$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(HigherEducated$Trust, HigherEducated$Surveytype), 2) prop.table (table(HigherEducated$Unieduc, HigherEducated$Surveytype), 2) prop.table (table(HigherEducated$PolLead, HigherEducated$Surveytype), 2) prop.table (table(HigherEducated$Trustpol, HigherEducated$Surveytype), 2) aggregate(HigherEducated$Trust ~ Surveytype, HigherEducated, length) aggregate(HigherEducated$Unieduc ~ Surveytype, HigherEducated, length) aggregate(HigherEducated$PolLead ~ Surveytype, HigherEducated, length) aggregate(HigherEducated$Trustpol ~ Surveytype, HigherEducated, length) ################################################ #################HigherEducated NO 2############ ################################################ ABWVS20132018$Educcats2[ABWVS20132018$Educ < 2] <- 0 ABWVS20132018$Educcats2[ABWVS20132018$Educ > 1] <- 1 table(ABWVS20132018$Educcats2) HigherEducated2 <- ABWVS20132018[ABWVS20132018$Educcats2 == 1, ] table(HigherEducated2$Educ) table(ABWVS20132018$Educ) summary(HigherEducated2$Surveytype) aggregate(HigherEducated2$Trust ~ Surveytype, HigherEducated2, mean) chisq.test(table(HigherEducated2$Trust,HigherEducated2$Surveytype)) t.test(HigherEducated2$Unieduc ~ HigherEducated2$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(HigherEducated2$PolLead ~ HigherEducated2$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(HigherEducated2$Trustpol ~ HigherEducated2$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(HigherEducated2$Trust, HigherEducated2$Surveytype), 2) prop.table (table(HigherEducated2$Unieduc, HigherEducated2$Surveytype), 2) prop.table (table(HigherEducated2$PolLead, HigherEducated2$Surveytype), 2) prop.table (table(HigherEducated2$Trustpol, HigherEducated2$Surveytype), 2) aggregate(HigherEducated2$Trust ~ Surveytype, HigherEducated2, length) aggregate(HigherEducated2$Unieduc ~ Surveytype, HigherEducated2, length) aggregate(HigherEducated2$PolLead ~ Surveytype, HigherEducated2, length) aggregate(HigherEducated2$Trustpol ~ Surveytype, HigherEducated2, length) ################################################################################################################## ################################################################################################################## #########################################PERCENT MATCH ANALYSES 2013############################################## ################################################################################################################## ################################################################################################################## ################################################ #################ID############################# ################################################ dmAB3$Survey <- rep("AB", 13583) table(dmAB3$Survey) dmWV6$Survey <- rep("WVS", 14087) table(dmWV6$Survey) dmAB3$id <- c(1:13583) summary(dmAB3$id) length(dmWV6$Survey) dmWV6$id <- c(13584:27670) summary(dmWV6$id) # Merging AB and WVS, all variables, full datasets--------------------------------------------------- table(dmWV6$id) ABWVS <- merge(dmAB3, dmWV6, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id"), all=TRUE) names(ABWVS) summary(ABWVS$id) table(ABWVS$Empl) table(ABWVS$survey) table(ABWVS$country) summary(ABWVS$Surveytype) ABWVS$Surveytype <- as.factor (ABWVS$Surveytype) summary(ABWVS$Surveytype) table(ABWVS$Surveytype) ABWVS$Surveytype2[ABWVS$Surveytype == 1] <- 0 ABWVS$Surveytype2[ABWVS$Surveytype == 0] <- 1 table(ABWVS$Surveytype2) #Surveytype has 0 WVS, 1 AB // Surveytype2 has 0 AB, 1 WVS # Merging AB and WVS, only needed variables, small datasets -------------------------------------------------- names(dmAB3) AB_short <- dmAB3 %>% select(Surveytype, Survey, Empl, Mar, Educ, age_short, gender, country, Trust, Unieduc, PolLead, Trustpol, id) names(AB_short) summary(AB_short) names(dmWV6) WVS_short <- dmWV6 %>% select(Surveytype, Survey, Empl, Mar, Educ, age_short, gender, country, Trust, Unieduc, PolLead, Trustpol, id) names(WVS_short) summary(WVS_short) ABWVS_short <- merge(WVS_short, AB_short, all = TRUE) summary(ABWVS_short) summary(ABWVS_short$Surveytype) table(ABWVS_short$Surveytype) ABWVS_short$Surveytype <- as.factor(ABWVS_short$Surveytype) summary(ABWVS_short$Surveytype) table(ABWVS_short$Surveytype) getwd() #setwd("C:/R"). library(tidyverse) library(Matrix) library(lme4) library(foreign) library(ggplot2) detach("package:memisc", unload = TRUE) ################################################### #############ALGERIA: PERCENT MATCH################ ############################AB##################### ################################################### names(ABWVS) table(ABWVS$surveytype) table(ABWVS$country) table(ABWVS$V2) AlgeriaAB <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Algeria") AlgeriaABmat <- as.matrix(AlgeriaAB) percent_match_AlgeriaAB <- c() for(i in seq(nrow(AlgeriaABmat))){ vec <- AlgeriaABmat[i,] dat <- AlgeriaABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_AlgeriaAB[i] <- max(diff) } qplot(percent_match_AlgeriaAB) summary(percent_match_AlgeriaAB) AlgeriaAB2 <- as_tibble(AlgeriaABmat) is.numeric(percent_match_AlgeriaAB) AlgeriaAB2$matchover85AlgeriaAB <- rep(NA,1218) AlgeriaAB2$matchover85AlgeriaAB[percent_match_AlgeriaAB <= 0.849] <-0 AlgeriaAB2$matchover85AlgeriaAB[percent_match_AlgeriaAB >= 0.850] <-1 table(AlgeriaAB2$matchover85AlgeriaAB) #8 cases have 1 which makes 0.006557377 prop.table(table(AlgeriaAB2$matchover85AlgeriaAB)) ##########ALGERIA: PERCENT MATCH################ ###############WVS##################### names(ABWVS) table(ABWVS$surveytype) table(ABWVS$country) table(ABWVS$V2) AlgeriaWVS <- ABWVS %>% mutate(country = as.factor(V2)) %>% filter(country == "Algeria") table(AlgeriaWVS$country) table(AlgeriaWVS$gender) table(AlgeriaWVS$Surveytype) #1199 respondents; 608 male, 591 female. AlgeriaWVSmat <- as.matrix(AlgeriaWVS) percent_match_AlgeriaWVS <- c() for(i in seq(nrow(AlgeriaWVSmat))){ vec <- AlgeriaWVSmat[i,] dat <- AlgeriaWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_AlgeriaWVS[i] <- max(diff) } qplot(percent_match_AlgeriaWVS) summary(percent_match_AlgeriaWVS) detach("package:memisc", unload = TRUE) AlgeriaWVS2 <- as_tibble(AlgeriaWVSmat) AlgeriaWVS2$matchover85AlgeriaWVS <- rep(NA,1199) AlgeriaWVS2$matchover85AlgeriaWVS[percent_match_AlgeriaWVS <= 0.849] <-0 AlgeriaWVS2$matchover85AlgeriaWVS[percent_match_AlgeriaWVS >= 0.850] <-1 table(AlgeriaWVS2$matchover85AlgeriaWVS) #SASKIA HAS: 229 cases over 85, 19.2%. prop.table(table(AlgeriaWVS2$matchover85AlgeriaWVS)) ##########ALGERIA: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(AlgeriaWVS2) AlgeriaWVS2$percentmatch <- as.character(AlgeriaWVS2$matchover85AlgeriaWVS) table(AlgeriaWVS2$percentmatch) table(AlgeriaWVS2$gender) #AB. names(AlgeriaAB2) AlgeriaAB2$percentmatch <- as.character(AlgeriaAB2$matchover85AlgeriaAB) table(AlgeriaAB2$percentmatch) table(AlgeriaAB2$gender) AlgeriaABWVS <- merge(AlgeriaWVS2, AlgeriaAB2, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(AlgeriaABWVS$country) table(AlgeriaABWVS$percentmatch) table(AlgeriaABWVS$Surveytype) AlgeriaABWVS$Trust <- as.numeric(AlgeriaABWVS$Trust) table(AlgeriaABWVS$age_short) AlgeriaABWVS$age_short <- as.numeric(AlgeriaABWVS$age_short) table(AlgeriaABWVS$age_short) table(AlgeriaABWVS$Educ) AlgeriaABWVS$Educ <- as.numeric(AlgeriaABWVS$Educ) table(AlgeriaABWVS$Educ) AlgeriaABWVS$Surveytype2[AlgeriaABWVS$Surveytype == 1] <- 0 AlgeriaABWVS$Surveytype2[AlgeriaABWVS$Surveytype == 0] <- 1 table(AlgeriaABWVS$Surveytype2) #########################################ALGERIA: PERCENT MATCH################ ###############re-running regressions ALL CASES IN ALGERIA TO CHECK##################### library(lmerTest) library(lmtest) mAlgeriaABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = AlgeriaABWVS, family = binomial) summary(mAlgeriaABWVS1a) mAlgeriaABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = AlgeriaABWVS, family = binomial) summary(mAlgeriaABWVS1b) mAlgeriaABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = AlgeriaABWVS, family = binomial) summary(mAlgeriaABWVS1c) mAlgeriaABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = AlgeriaABWVS, family = binomial) summary(mAlgeriaABWVS1c2) mAlgeriaABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS, family = binomial) summary(mAlgeriaABWVS1d) mAlgeriaABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS, family = binomial) summary(mAlgeriaABWVS1e) mAlgeriaABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVS, family = binomial) summary(mAlgeriaABWVS1e2) mAlgeriaABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS, family = binomial) summary(mAlgeriaABWVS1f) mAlgeriaABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVS, family = binomial) summary(mAlgeriaABWVS1f2) table(AlgeriaABWVS$Unieduc) table(AlgeriaABWVS$country) mAlgeriaABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS2a) mAlgeriaABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS2b) mAlgeriaABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS2c) mAlgeriaABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS2d) mAlgeriaABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS2e) mAlgeriaABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS2f) mAlgeriaABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS2f2) table(AlgeriaABWVS$PolLead) table(AlgeriaABWVS$country) mAlgeriaABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS3a) mAlgeriaABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS3b) mAlgeriaABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS3c) mAlgeriaABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS3d) mAlgeriaABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS3e) mAlgeriaABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS3f) table(AlgeriaABWVS$Trustpol) table(AlgeriaABWVS$country) mAlgeriaABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS4a) mAlgeriaABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS4b) mAlgeriaABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS4c) mAlgeriaABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS4d) mAlgeriaABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS4d2) mAlgeriaABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS4e) mAlgeriaABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS4f) mAlgeriaABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVS) summary(mAlgeriaABWVS4f2) #########################################ALGERIA: PERCENT MATCH################ ###############regressions WITHOUT FRAUDULENT CASES##################### table(AlgeriaABWVS$country) table(AlgeriaABWVS$percentmatch) AlgeriaABWVSwofraude <- subset(AlgeriaABWVS, percentmatch!= "1") table(AlgeriaABWVSwofraude$percentmatch) table(AlgeriaABWVSwofraude$country) table(AlgeriaABWVS$Surveytype) mAlgeriaABWVSwofraude1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude, family = binomial) summary(mAlgeriaABWVSwofraude1a) mAlgeriaABWVSwofraude1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = AlgeriaABWVSwofraude, family = binomial) summary(mAlgeriaABWVSwofraude1b) mAlgeriaABWVSwofraude1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = AlgeriaABWVSwofraude, family = binomial) summary(mAlgeriaABWVSwofraude1c) mAlgeriaABWVSwofraude1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = AlgeriaABWVSwofraude, family = binomial) summary(mAlgeriaABWVSwofraude1c2) mAlgeriaABWVSwofraude1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude, family = binomial) summary(mAlgeriaABWVSwofraude1d) mAlgeriaABWVSwofraude1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude, family = binomial) summary(mAlgeriaABWVSwofraude1e) mAlgeriaABWVSwofraude1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude, family = binomial) summary(mAlgeriaABWVSwofraude1e2) mAlgeriaABWVSwofraude1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude, family = binomial) summary(mAlgeriaABWVSwofraude1f) mAlgeriaABWVSwofraude1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude, family = binomial) summary(mAlgeriaABWVSwofraude1f2) table(AlgeriaABWVSwofraude$Unieduc) table(AlgeriaABWVSwofraude$country) mAlgeriaABWVSwofraude2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude2a) mAlgeriaABWVSwofraude2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude2b) mAlgeriaABWVSwofraude2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude2c) mAlgeriaABWVSwofraude2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude2c2) mAlgeriaABWVSwofraude2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude2d) mAlgeriaABWVSwofraude2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude2e) mAlgeriaABWVSwofraude2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude2f) mAlgeriaABWVSwofraude2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude2f2) table(AlgeriaABWVSwofraude$PolLead) table(AlgeriaABWVSwofraude$country) mAlgeriaABWVSwofraude3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude3a) mAlgeriaABWVSwofraude3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude3b) mAlgeriaABWVSwofraude3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude3c) mAlgeriaABWVSwofraude3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude3c2) mAlgeriaABWVSwofraude3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude3d) mAlgeriaABWVSwofraude3d2 <- lm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude3d2) mAlgeriaABWVSwofraude3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude3e) mAlgeriaABWVSwofraude3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude3f) table(AlgeriaABWVSwofraude$Trustpol) table(AlgeriaABWVSwofraude$country) mAlgeriaABWVSwofraude4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude4a) mAlgeriaABWVSwofraude4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude4b) mAlgeriaABWVSwofraude4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude4c) mAlgeriaABWVSwofraude4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude4d) mAlgeriaABWVSwofraude4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude4d2) mAlgeriaABWVSwofraude4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude4e) mAlgeriaABWVSwofraude4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude4f) mAlgeriaABWVSwofraude4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = AlgeriaABWVSwofraude) summary(mAlgeriaABWVSwofraude4f2) ################################################### ##############EGYPT: PERCENT MATCH################# ###########################AB###################### ################################################### table(dmAB3$country) Egypt <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Egypt") EgyptABmat <- as.matrix(Egypt) percent_match_EgyptAB <- c() for(i in seq(nrow(EgyptABmat))){ vec <- EgyptABmat[i,] dat <- EgyptABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_EgyptAB[i] <- max(diff) } qplot(percent_match_EgyptAB) summary(percent_match_EgyptAB) EgyptAB3 <- as_tibble(EgyptABmat) is.numeric(percent_match_EgyptAB) EgyptAB3$matchover85EgyptAB <- rep(NA,1196) EgyptAB3$matchover85EgyptAB[percent_match_EgyptAB <= 0.849] <-0 EgyptAB3$matchover85EgyptAB[percent_match_EgyptAB >= 0.850] <-1 table(EgyptAB3$matchover85EgyptAB) # 210 cases, 17.6% prop.table(table(EgyptAB3$matchover85EgyptAB)) ##########EGYPT: PERCENT MATCH################ ###############WVS##################### EgyptWVS <- dmWV6 %>% mutate(country = as.factor(V2)) %>% filter(country == "Egypt") EgyptWVSmat <- as.matrix(EgyptWVS) percent_match_EgyptWVS <- c() for(i in seq(nrow(EgyptWVSmat))){ vec <- EgyptWVSmat[i,] dat <- EgyptWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_EgyptWVS[i] <- max(diff) } qplot(percent_match_EgyptWVS) summary(percent_match_EgyptWVS) EgyptWVS2 <- as_tibble(EgyptWVSmat) EgyptWVS2$matchover85EgyptWVS <- rep(NA,1518) EgyptWVS2$matchover85EgyptWVS[percent_match_EgyptWVS <= 0.849] <-0 EgyptWVS2$matchover85EgyptWVS[percent_match_EgyptWVS >= 0.850] <-1 table(EgyptWVS2$matchover85EgyptWVS) # 79 cases, 5,2% prop.table(table(EgyptWVS2$matchover85EgyptWVS)) ##########EGYPT: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(EgyptWVS2) EgyptWVS2$percentmatch <- as.character(EgyptWVS2$matchover85EgyptWVS) table(EgyptWVS2$percentmatch) table(EgyptWVS2$gender) #AB. names(EgyptAB3) EgyptAB3$percentmatch <- as.character(EgyptAB3$matchover85EgyptAB) table(EgyptAB3$percentmatch) table(EgyptAB3$gender) EgyptABWVS <- merge(EgyptWVS2, EgyptAB3, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(EgyptABWVS$country) table(EgyptABWVS$percentmatch) table(EgyptABWVS$Surveytype) EgyptABWVS$Trust <- as.numeric(EgyptABWVS$Trust) table(EgyptABWVS$age_short) EgyptABWVS$age_short <- as.numeric(EgyptABWVS$age_short) table(EgyptABWVS$age_short) table(EgyptABWVS$Educ) EgyptABWVS$Educ <- as.numeric(EgyptABWVS$Educ) table(EgyptABWVS$Educ) EgyptABWVS$Surveytype2[EgyptABWVS$Surveytype == 1] <- 0 EgyptABWVS$Surveytype2[EgyptABWVS$Surveytype == 0] <- 1 table(EgyptABWVS$Surveytype2) #########################################EGYPT: PERCENT MATCH################ ###############re-running regressions ALL CASES IN EGYPT TO CHECK##################### mEgyptABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = EgyptABWVS, family = binomial) summary(mEgyptABWVS1a) mEgyptABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = EgyptABWVS, family = binomial) summary(mEgyptABWVS1b) mEgyptABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = EgyptABWVS, family = binomial) summary(mEgyptABWVS1c) mEgyptABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = EgyptABWVS, family = binomial) summary(mEgyptABWVS1c2) mEgyptABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS, family = binomial) summary(mEgyptABWVS1d) mEgyptABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS, family = binomial) summary(mEgyptABWVS1e) mEgyptABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVS, family = binomial) summary(mEgyptABWVS1e2) mEgyptABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS, family = binomial) summary(mEgyptABWVS1f) mEgyptABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVS, family = binomial) summary(mEgyptABWVS1f2) table(EgyptABWVS$Unieduc) table(EgyptABWVS$country) mEgyptABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS2a) mEgyptABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS2b) mEgyptABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS2c) mEgyptABWVS2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS2c2) mEgyptABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS2d) mEgyptABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS2e) mEgyptABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS2f) mEgyptABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS2f2) table(EgyptABWVS$PolLead) table(EgyptABWVS$country) mEgyptABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS3a) mEgyptABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS3b) mEgyptABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS3c) mEgyptABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS3d) mEgyptABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS3e) mEgyptABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS3f) table(EgyptABWVS$Trustpol) table(EgyptABWVS$country) mEgyptABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS4a) mEgyptABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS4b) mEgyptABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS4c) mEgyptABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS4d) mEgyptABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS4d2) mEgyptABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS4e) mEgyptABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS4f) mEgyptABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVS) summary(mEgyptABWVS4f2) #########################################EGYPT: PERCENT MATCH################ ###############regressions WITHOUT FRAUDULENT CASES##################### table(EgyptABWVS$country) table(EgyptABWVS$percentmatch) EgyptABWVSwofraude <- subset(EgyptABWVS, percentmatch!= "1") table(EgyptABWVSwofraude$percentmatch) table(EgyptABWVSwofraude$country) table(EgyptABWVS$Surveytype) mEgyptABWVSwofraude1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1a) mEgyptABWVSwofraude1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1b) mEgyptABWVSwofraude1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1c) mEgyptABWVSwofraude1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1c2) mEgyptABWVSwofraude1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1d) mEgyptABWVSwofraude1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1e) mEgyptABWVSwofraude1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1e2) mEgyptABWVSwofraude1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1f) mEgyptABWVSwofraude1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1f2) table(EgyptABWVSwofraude$Unieduc) table(EgyptABWVSwofraude$country) mEgyptABWVSwofraude2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2a) mEgyptABWVSwofraude2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2b) mEgyptABWVSwofraude2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2c) mEgyptABWVSwofraude2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2c2) mEgyptABWVSwofraude2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2d) mEgyptABWVSwofraude2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2e) mEgyptABWVSwofraude2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2e2) mEgyptABWVSwofraude2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2f) mEgyptABWVSwofraude2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2f2) table(EgyptABWVSwofraude$PolLead) table(EgyptABWVSwofraude$country) mEgyptABWVSwofraude3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3a) mEgyptABWVSwofraude3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3b) mEgyptABWVSwofraude3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3c) mEgyptABWVSwofraude3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3c2) mEgyptABWVSwofraude3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3d) mEgyptABWVSwofraude3d2 <- lm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3d2) mEgyptABWVSwofraude3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3e) mEgyptABWVSwofraude3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3f) table(EgyptABWVSwofraude$Trustpol) table(EgyptABWVSwofraude$country) mEgyptABWVSwofraude4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude4a) mEgyptABWVSwofraude4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude4b) mEgyptABWVSwofraude4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude4c) mEgyptABWVSwofraude4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude4d) mEgyptABWVSwofraude4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude4d2) mEgyptABWVSwofraude4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude4e) mEgyptABWVSwofraude4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude4e2) mEgyptABWVSwofraude4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude4f) mEgyptABWVSwofraude4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude4f2) ################################################### ################IRAQ: PERCENT MATCH################ ############################AB##################### ################################################## table(dmAB3$country) Iraq <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Iraq") IraqABmat <- as.matrix(Iraq) percent_match_IraqAB <- c() for(i in seq(nrow(IraqABmat))){ vec <- IraqABmat[i,] dat <- IraqABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_IraqAB[i] <- max(diff) } qplot(percent_match_IraqAB) summary(percent_match_IraqAB) IraqAB3 <- as_tibble(IraqABmat) is.numeric(percent_match_IraqAB) IraqAB3$matchover85IraqAB <- rep(NA,1215) IraqAB3$matchover85IraqAB[percent_match_IraqAB <= 0.849] <-0 IraqAB3$matchover85IraqAB[percent_match_IraqAB >= 0.850] <-1 table(IraqAB3$matchover85IraqAB) # 80 cases, 0.65% prop.table(table(IraqAB3$matchover85IraqAB)) ##########Iraq: PERCENT MATCH################ ###############WVS##################### IraqWVS <- dmWV6 %>% mutate(country = as.factor(V2)) %>% filter(country == "Iraq") IraqWVSmat <- as.matrix(IraqWVS) percent_match_IraqWVS <- c() for(i in seq(nrow(IraqWVSmat))){ vec <- IraqWVSmat[i,] dat <- IraqWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_IraqWVS[i] <- max(diff) } qplot(percent_match_IraqWVS) summary(percent_match_IraqWVS) IraqWVS2 <- as_tibble(IraqWVSmat) IraqWVS2$matchover85IraqWVS <- rep(NA,1199) IraqWVS2$matchover85IraqWVS[percent_match_IraqWVS <= 0.849] <-0 IraqWVS2$matchover85IraqWVS[percent_match_IraqWVS >= 0.850] <-1 table(IraqWVS2$matchover85IraqWVS) # 26 cases, 2 % prop.table(table(IraqWVS2$matchover85IraqWVS)) ##########IRAQ: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(IraqWVS2) IraqWVS2$percentmatch <- as.character(IraqWVS2$matchover85IraqWVS) table(IraqWVS2$percentmatch) table(IraqWVS2$gender) #AB. names(IraqAB3) IraqAB3$percentmatch <- as.character(IraqAB3$matchover85IraqAB) table(IraqAB3$percentmatch) table(IraqAB3$gender) IraqABWVS <- merge(IraqWVS2, IraqAB3, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(IraqABWVS$country) table(IraqABWVS$percentmatch) table(IraqABWVS$Surveytype) IraqABWVS$Trust <- as.numeric(IraqABWVS$Trust) table(IraqABWVS$age_short) IraqABWVS$age_short <- as.numeric(IraqABWVS$age_short) table(IraqABWVS$age_short) table(IraqABWVS$Educ) IraqABWVS$Educ <- as.numeric(IraqABWVS$Educ) table(IraqABWVS$Educ) IraqABWVS$Surveytype2[IraqABWVS$Surveytype == 1] <- 0 IraqABWVS$Surveytype2[IraqABWVS$Surveytype == 0] <- 1 table(IraqABWVS$Surveytype2) #########################################IRAQ: PERCENT MATCH################ ###############re-running regressions ALL CASES IN IRAQ TO CHECK##################### mIraqABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = IraqABWVS, family = binomial) summary(mIraqABWVS1a) mIraqABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = IraqABWVS, family = binomial) summary(mIraqABWVS1b) mIraqABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = IraqABWVS, family = binomial) summary(mIraqABWVS1c) mIraqABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = IraqABWVS, family = binomial) summary(mIraqABWVS1c2) mIraqABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS, family = binomial) summary(mIraqABWVS1d) mIraqABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS, family = binomial) summary(mIraqABWVS1e) mIraqABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVS, family = binomial) summary(mIraqABWVS1e2) mIraqABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS, family = binomial) summary(mIraqABWVS1f) mIraqABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVS, family = binomial) summary(mIraqABWVS1f2) table(IraqABWVS$Unieduc) table(IraqABWVS$country) mIraqABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS2a) mIraqABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS2b) mIraqABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS2c) mIraqABWVS2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS2c2) mIraqABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS2d) mIraqABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS2e) mIraqABWVS2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS2e2) mIraqABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS2f) mIraqABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS2f2) table(IraqABWVS$PolLead) table(IraqABWVS$country) mIraqABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS3a) mIraqABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS3b) mIraqABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS3c) mIraqABWVS3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS3c2) mIraqABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS3d) mIraqABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS3e) mIraqABWVS3e2 <- lm(PolLead ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS3e2) mIraqABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS3f) table(IraqABWVS$Trustpol) table(IraqABWVS$country) mIraqABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS4a) mIraqABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS4b) mIraqABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS4c) mIraqABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS4d) mIraqABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS4d2) mIraqABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS4e) mIraqABWVS4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS4e2) mIraqABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS4f) mIraqABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVS) summary(mIraqABWVS4f2) #########################################IRAQ: PERCENT MATCH################ ###############regressions WITHOUT FRAUDULENT CASES##################### table(IraqABWVS$country) table(IraqABWVS$percentmatch) IraqABWVSwofraude <- subset(IraqABWVS, percentmatch!= "1") table(IraqABWVSwofraude$percentmatch) table(IraqABWVSwofraude$country) table(IraqABWVS$Surveytype) mIraqABWVSwofraude1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude, family = binomial) summary(mIraqABWVSwofraude1a) mIraqABWVSwofraude1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = IraqABWVSwofraude, family = binomial) summary(mIraqABWVSwofraude1b) mIraqABWVSwofraude1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = IraqABWVSwofraude, family = binomial) summary(mIraqABWVSwofraude1c) mIraqABWVSwofraude1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = IraqABWVSwofraude, family = binomial) summary(mIraqABWVSwofraude1c2) mIraqABWVSwofraude1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude, family = binomial) summary(mIraqABWVSwofraude1d) mIraqABWVSwofraude1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude, family = binomial) summary(mIraqABWVSwofraude1e) mIraqABWVSwofraude1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude, family = binomial) summary(mIraqABWVSwofraude1e2) mIraqABWVSwofraude1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude, family = binomial) summary(mIraqABWVSwofraude1f) mIraqABWVSwofraude1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude, family = binomial) summary(mIraqABWVSwofraude1f2) table(IraqABWVSwofraude$Unieduc) table(IraqABWVSwofraude$country) mIraqABWVSwofraude2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude2a) mIraqABWVSwofraude2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude2b) mIraqABWVSwofraude2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude2c) mIraqABWVSwofraude2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude2c2) mIraqABWVSwofraude2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude2d) mIraqABWVSwofraude2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude2e) mIraqABWVSwofraude2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude2e2) mIraqABWVSwofraude2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude2f) mIraqABWVSwofraude2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude2f2) table(IraqABWVSwofraude$PolLead) table(IraqABWVSwofraude$country) mIraqABWVSwofraude3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude3a) mIraqABWVSwofraude3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude3b) mIraqABWVSwofraude3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude3c) mIraqABWVSwofraude3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude3c2) mIraqABWVSwofraude3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude3d) mIraqABWVSwofraude3d2 <- lm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude3d2) mIraqABWVSwofraude3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude3e) mIraqABWVSwofraude3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude3f) table(IraqABWVSwofraude$Trustpol) table(IraqABWVSwofraude$country) mIraqABWVSwofraude4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude4a) mIraqABWVSwofraude4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude4b) mIraqABWVSwofraude4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude4c) mIraqABWVSwofraude4c2 <- lm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude4c2) mIraqABWVSwofraude4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude4d) mIraqABWVSwofraude4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude4d2) mIraqABWVSwofraude4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude4e) mIraqABWVSwofraude4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude4e2) mIraqABWVSwofraude4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude4f) mIraqABWVSwofraude4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = IraqABWVSwofraude) summary(mIraqABWVSwofraude4f2) ################################################### ##########Kuwait: PERCENT MATCH################ ######################AB##################### ################################################ table(dmAB3$country) Kuwait <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Kuwait") KuwaitABmat <- as.matrix(Kuwait) percent_match_KuwaitAB <- c() for(i in seq(nrow(KuwaitABmat))){ vec <- KuwaitABmat[i,] dat <- KuwaitABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_KuwaitAB[i] <- max(diff) } qplot(percent_match_KuwaitAB) summary(percent_match_KuwaitAB) KuwaitAB3 <- as_tibble(KuwaitABmat) is.numeric(percent_match_KuwaitAB) KuwaitAB3$matchover85KuwaitAB <- rep(NA,1019) KuwaitAB3$matchover85KuwaitAB[percent_match_KuwaitAB <= 0.849] <-0 KuwaitAB3$matchover85KuwaitAB[percent_match_KuwaitAB >= 0.850] <-1 table(KuwaitAB3$matchover85KuwaitAB) # 0% prop.table(table(KuwaitAB3$matchover85KuwaitAB)) ##########Kuwait: PERCENT MATCH################ ###############WVS##################### KuwaitWVS <- dmWV6 %>% mutate(country = as.factor(V2)) %>% filter(country == "Kuwait") KuwaitWVSmat <- as.matrix(KuwaitWVS) percent_match_KuwaitWVS <- c() for(i in seq(nrow(KuwaitWVSmat))){ vec <- KuwaitWVSmat[i,] dat <- KuwaitWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_KuwaitWVS[i] <- max(diff) } qplot(percent_match_KuwaitWVS) summary(percent_match_KuwaitWVS) KuwaitWVS2 <- as_tibble(KuwaitWVSmat) KuwaitWVS2$matchover85KuwaitWVS <- rep(NA,1243) KuwaitWVS2$matchover85KuwaitWVS[percent_match_KuwaitWVS <= 0.849] <-0 KuwaitWVS2$matchover85KuwaitWVS[percent_match_KuwaitWVS >= 0.850] <-1 table(KuwaitWVS2$matchover85KuwaitWVS) #84 cases, 6.7% prop.table(table(KuwaitWVS2$matchover85KuwaitWVS)) ##########KUWAIT: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(KuwaitWVS2) KuwaitWVS2$percentmatch <- as.character(KuwaitWVS2$matchover85KuwaitWVS) table(KuwaitWVS2$percentmatch) table(KuwaitWVS2$gender) #AB. names(KuwaitAB3) KuwaitAB3$percentmatch <- as.character(KuwaitAB3$matchover85KuwaitAB) table(KuwaitAB3$percentmatch) table(KuwaitAB3$gender) KuwaitABWVS <- merge(KuwaitWVS2, KuwaitAB3, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(KuwaitABWVS$country) table(KuwaitABWVS$percentmatch) table(KuwaitABWVS$Surveytype) KuwaitABWVS$Trust <- as.numeric(KuwaitABWVS$Trust) table(KuwaitABWVS$age_short) KuwaitABWVS$age_short <- as.numeric(KuwaitABWVS$age_short) table(KuwaitABWVS$age_short) table(KuwaitABWVS$Educ) KuwaitABWVS$Educ <- as.numeric(KuwaitABWVS$Educ) table(KuwaitABWVS$Educ) table(KuwaitABWVS$gender) KuwaitABWVS$gender <- as.numeric(KuwaitABWVS$gender) table(KuwaitABWVS$gender) table(KuwaitABWVS$Empl) KuwaitABWVS$Empl <- as.numeric(KuwaitABWVS$Empl) table(KuwaitABWVS$Empl) KuwaitABWVS$Surveytype2[KuwaitABWVS$Surveytype == 1] <- 0 KuwaitABWVS$Surveytype2[KuwaitABWVS$Surveytype == 0] <- 1 table(KuwaitABWVS$Surveytype2) #########################################KUWAIT: PERCENT MATCH################ ###############re-running regressions ALL CASES IN KUWAIT TO CHECK##################### mKuwaitABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = KuwaitABWVS, family = binomial) summary(mKuwaitABWVS1a) mKuwaitABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = KuwaitABWVS, family = binomial) summary(mKuwaitABWVS1b) mKuwaitABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = KuwaitABWVS, family = binomial) summary(mKuwaitABWVS1c) mKuwaitABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = KuwaitABWVS, family = binomial) summary(mKuwaitABWVS1c2) mKuwaitABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS, family = binomial) summary(mKuwaitABWVS1d) mKuwaitABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS, family = binomial) summary(mKuwaitABWVS1e) mKuwaitABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVS, family = binomial) summary(mKuwaitABWVS1e2) mKuwaitABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS, family = binomial) summary(mKuwaitABWVS1f) mKuwaitABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVS, family = binomial) summary(mKuwaitABWVS1f2) table(KuwaitABWVS$Unieduc) table(KuwaitABWVS$country) mKuwaitABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS2a) mKuwaitABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS2b) mKuwaitABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS2c) mKuwaitABWVS2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS2c2) mKuwaitABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS2d) mKuwaitABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS2e) mKuwaitABWVS2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS2e2) mKuwaitABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS2f) mKuwaitABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS2f2) table(KuwaitABWVS$PolLead) table(KuwaitABWVS$country) mKuwaitABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS3a) mKuwaitABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS3b) mKuwaitABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS3c) mKuwaitABWVS3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS3c2) mKuwaitABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS3d) mKuwaitABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS3e) mKuwaitABWVS3e2 <- lm(PolLead ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS3e2) mKuwaitABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS3f) table(KuwaitABWVS$Trustpol) table(KuwaitABWVS$country) mKuwaitABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS4a) mKuwaitABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS4b) mKuwaitABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS4c) mKuwaitABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS4d) mKuwaitABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS4d2) mKuwaitABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS4e) mKuwaitABWVS4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS4e2) mKuwaitABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS4f) mKuwaitABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVS) summary(mKuwaitABWVS4f2) #########################################KUWAIT: PERCENT MATCH################ ###############regressions WITHOUT FRAUDULENT CASES##################### table(KuwaitABWVS$country) table(KuwaitABWVS$percentmatch) KuwaitABWVSwofraude <- subset(KuwaitABWVS, percentmatch!= "1") table(KuwaitABWVSwofraude$percentmatch) table(KuwaitABWVSwofraude$country) table(KuwaitABWVS$Surveytype) table(KuwaitABWVS$gender) mKuwaitABWVSwofraude1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude, family = binomial) summary(mKuwaitABWVSwofraude1a) mKuwaitABWVSwofraude1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = KuwaitABWVSwofraude, family = binomial) summary(mKuwaitABWVSwofraude1b) mKuwaitABWVSwofraude1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = KuwaitABWVSwofraude, family = binomial) summary(mKuwaitABWVSwofraude1c) mKuwaitABWVSwofraude1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = KuwaitABWVSwofraude, family = binomial) summary(mKuwaitABWVSwofraude1c2) mKuwaitABWVSwofraude1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude, family = binomial) summary(mKuwaitABWVSwofraude1d) mKuwaitABWVSwofraude1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude, family = binomial) summary(mKuwaitABWVSwofraude1e) mKuwaitABWVSwofraude1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude, family = binomial) summary(mKuwaitABWVSwofraude1e2) mKuwaitABWVSwofraude1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude, family = binomial) summary(mKuwaitABWVSwofraude1f) mKuwaitABWVSwofraude1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude, family = binomial) summary(mKuwaitABWVSwofraude1f2) table(KuwaitABWVSwofraude$Unieduc) table(KuwaitABWVSwofraude$country) mKuwaitABWVSwofraude2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude2a) mKuwaitABWVSwofraude2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude2b) mKuwaitABWVSwofraude2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude2c) mKuwaitABWVSwofraude2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude2c2) mKuwaitABWVSwofraude2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude2d) mKuwaitABWVSwofraude2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude2e) mKuwaitABWVSwofraude2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude2e2) mKuwaitABWVSwofraude2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude2f) mKuwaitABWVSwofraude2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude2f2) table(KuwaitABWVSwofraude$PolLead) table(KuwaitABWVSwofraude$country) mKuwaitABWVSwofraude3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude3a) mKuwaitABWVSwofraude3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude3b) mKuwaitABWVSwofraude3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude3c) mKuwaitABWVSwofraude3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude3c2) mKuwaitABWVSwofraude3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude3d) mKuwaitABWVSwofraude3d2 <- lm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude3d2) mKuwaitABWVSwofraude3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude3e) mKuwaitABWVSwofraude3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude3f) table(KuwaitABWVSwofraude$Trustpol) table(KuwaitABWVSwofraude$country) mKuwaitABWVSwofraude4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude4a) mKuwaitABWVSwofraude4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude4b) mKuwaitABWVSwofraude4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude4c) mKuwaitABWVSwofraude4c2 <- lm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude4c2) mKuwaitABWVSwofraude4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude4d) mKuwaitABWVSwofraude4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude4d2) mKuwaitABWVSwofraude4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude4e) mKuwaitABWVSwofraude4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude4e2) mKuwaitABWVSwofraude4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude4f) mKuwaitABWVSwofraude4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = KuwaitABWVSwofraude) summary(mKuwaitABWVSwofraude4f2) ################################################### ##########Morocco: PERCENT MATCH################ ######################AB##################### ################################################ table(dmAB3$country) Morocco <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Morocco") MoroccoABmat <- as.matrix(Morocco) percent_match_MoroccoAB <- c() for(i in seq(nrow(MoroccoABmat))){ vec <- MoroccoABmat[i,] dat <- MoroccoABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_MoroccoAB[i] <- max(diff) } qplot(percent_match_MoroccoAB) summary(percent_match_MoroccoAB) MoroccoAB3 <- as_tibble(MoroccoABmat) is.numeric(percent_match_MoroccoAB) MoroccoAB3$matchover85MoroccoAB <- rep(NA,1116) MoroccoAB3$matchover85MoroccoAB[percent_match_MoroccoAB <= 0.849] <-0 MoroccoAB3$matchover85MoroccoAB[percent_match_MoroccoAB >= 0.850] <-1 table(MoroccoAB3$matchover85MoroccoAB) # prop.table(table(MoroccoAB3$matchover85MoroccoAB)) ##########Morocco: PERCENT MATCH################ ###############WVS##################### MoroccoWVS <- dmWV6 %>% mutate(country = as.factor(V2)) %>% filter(country == "Morocco") MoroccoWVSmat <- as.matrix(MoroccoWVS) percent_match_MoroccoWVS <- c() for(i in seq(nrow(MoroccoWVSmat))){ vec <- MoroccoWVSmat[i,] dat <- MoroccoWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_MoroccoWVS[i] <- max(diff) } qplot(percent_match_MoroccoWVS) summary(percent_match_MoroccoWVS) MoroccoWVS2 <- as_tibble(MoroccoWVSmat) MoroccoWVS2$matchover85MoroccoWVS <- rep(NA,1199) MoroccoWVS2$matchover85MoroccoWVS[percent_match_MoroccoWVS <= 0.849] <-0 MoroccoWVS2$matchover85MoroccoWVS[percent_match_MoroccoWVS >= 0.850] <-1 table(MoroccoWVS2$matchover85MoroccoWVS) # prop.table(table(MoroccoWVS2$matchover85MoroccoWVS)) table(MoroccoWVS2$matchover85MoroccoWVS, MoroccoWVS2$Educ) table(MoroccoWVS2$matchover85MoroccoWVS, MoroccoWVS2$age_short) ##########MOROCCO: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(MoroccoWVS2) MoroccoWVS2$percentmatch <- as.character(MoroccoWVS2$matchover85MoroccoWVS) table(MoroccoWVS2$percentmatch) table(MoroccoWVS2$gender) #AB. names(MoroccoAB3) MoroccoAB3$percentmatch <- as.character(MoroccoAB3$matchover85MoroccoAB) table(MoroccoAB3$percentmatch) table(MoroccoAB3$gender) MoroccoABWVS <- merge(MoroccoWVS2, MoroccoAB3, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(MoroccoABWVS$country) table(MoroccoABWVS$percentmatch) table(MoroccoABWVS$Surveytype) MoroccoABWVS$Trust <- as.numeric(MoroccoABWVS$Trust) table(MoroccoABWVS$age_short) MoroccoABWVS$age_short <- as.numeric(MoroccoABWVS$age_short) table(MoroccoABWVS$age_short) table(MoroccoABWVS$Educ) MoroccoABWVS$Educ <- as.numeric(MoroccoABWVS$Educ) table(MoroccoABWVS$Educ) table(MoroccoABWVS$gender) MoroccoABWVS$gender <- as.numeric(MoroccoABWVS$gender) table(MoroccoABWVS$gender) table(MoroccoABWVS$Empl) MoroccoABWVS$Empl <- as.numeric(MoroccoABWVS$Empl) table(MoroccoABWVS$Empl) MoroccoABWVS$Surveytype2[MoroccoABWVS$Surveytype == 1] <- 0 MoroccoABWVS$Surveytype2[MoroccoABWVS$Surveytype == 0] <- 1 table(MoroccoABWVS$Surveytype2) #########################################MOROCCO: PERCENT MATCH################ ###############re-running regressions ALL CASES IN MOROCCO TO CHECK##################### mMoroccoABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = MoroccoABWVS, family = binomial) summary(mMoroccoABWVS1a) mMoroccoABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = MoroccoABWVS, family = binomial) summary(mMoroccoABWVS1b) mMoroccoABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = MoroccoABWVS, family = binomial) summary(mMoroccoABWVS1c) mMoroccoABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = MoroccoABWVS, family = binomial) summary(mMoroccoABWVS1c2) mMoroccoABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS, family = binomial) summary(mMoroccoABWVS1d) mMoroccoABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS, family = binomial) summary(mMoroccoABWVS1e) mMoroccoABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVS, family = binomial) summary(mMoroccoABWVS1e2) mMoroccoABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS, family = binomial) summary(mMoroccoABWVS1f) mMoroccoABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVS, family = binomial) summary(mMoroccoABWVS1f2) table(MoroccoABWVS$Unieduc) table(MoroccoABWVS$country) mMoroccoABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS2a) mMoroccoABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS2b) mMoroccoABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS2c) mMoroccoABWVS2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS2c2) mMoroccoABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS2d) mMoroccoABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS2e) mMoroccoABWVS2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS2e2) mMoroccoABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS2f) mMoroccoABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS2f2) table(MoroccoABWVS$PolLead) table(MoroccoABWVS$country) mMoroccoABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS3a) mMoroccoABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS3b) mMoroccoABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS3c) mMoroccoABWVS3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS3c2) mMoroccoABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS3d) mMoroccoABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS3e) mMoroccoABWVS3e2 <- lm(PolLead ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS3e2) mMoroccoABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS3f) table(MoroccoABWVS$Trustpol) table(MoroccoABWVS$country) mMoroccoABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS4a) mMoroccoABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS4b) mMoroccoABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS4c) mMoroccoABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS4d) mMoroccoABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS4d2) mMoroccoABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS4e) mMoroccoABWVS4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS4e2) mMoroccoABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS4f) mMoroccoABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVS) summary(mMoroccoABWVS4f2) #########################################MOROCCO: PERCENT MATCH################ ###############regressions WITHOUT FRAUDULENT CASES##################### table(MoroccoABWVS$country) table(MoroccoABWVS$percentmatch) MoroccoABWVSwofraude <- subset(MoroccoABWVS, percentmatch!= "1") table(MoroccoABWVSwofraude$percentmatch) table(MoroccoABWVSwofraude$country) table(MoroccoABWVS$Surveytype) table(MoroccoABWVS$gender) mMoroccoABWVSwofraude1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude, family = binomial) summary(mMoroccoABWVSwofraude1a) mMoroccoABWVSwofraude1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = MoroccoABWVSwofraude, family = binomial) summary(mMoroccoABWVSwofraude1b) mMoroccoABWVSwofraude1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = MoroccoABWVSwofraude, family = binomial) summary(mMoroccoABWVSwofraude1c) mMoroccoABWVSwofraude1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = MoroccoABWVSwofraude, family = binomial) summary(mMoroccoABWVSwofraude1c2) mMoroccoABWVSwofraude1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude, family = binomial) summary(mMoroccoABWVSwofraude1d) mMoroccoABWVSwofraude1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude, family = binomial) summary(mMoroccoABWVSwofraude1e) mMoroccoABWVSwofraude1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude, family = binomial) summary(mMoroccoABWVSwofraude1e2) mMoroccoABWVSwofraude1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude, family = binomial) summary(mMoroccoABWVSwofraude1f) mMoroccoABWVSwofraude1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude, family = binomial) summary(mMoroccoABWVSwofraude1f2) table(MoroccoABWVSwofraude$Unieduc) table(MoroccoABWVSwofraude$country) mMoroccoABWVSwofraude2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude2a) mMoroccoABWVSwofraude2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude2b) mMoroccoABWVSwofraude2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude2c) mMoroccoABWVSwofraude2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude2c2) mMoroccoABWVSwofraude2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude2d) mMoroccoABWVSwofraude2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude2e) mMoroccoABWVSwofraude2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude2e2) mMoroccoABWVSwofraude2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude2f) mMoroccoABWVSwofraude2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude2f2) table(MoroccoABWVSwofraude$PolLead) table(MoroccoABWVSwofraude$country) mMoroccoABWVSwofraude3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude3a) mMoroccoABWVSwofraude3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude3b) mMoroccoABWVSwofraude3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude3c) mMoroccoABWVSwofraude3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude3c2) mMoroccoABWVSwofraude3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude3d) mMoroccoABWVSwofraude3d2 <- lm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude3d2) mMoroccoABWVSwofraude3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude3e) mMoroccoABWVSwofraude3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude3f) table(MoroccoABWVSwofraude$Trustpol) table(MoroccoABWVSwofraude$country) mMoroccoABWVSwofraude4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude4a) mMoroccoABWVSwofraude4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude4b) mMoroccoABWVSwofraude4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude4c) mMoroccoABWVSwofraude4c2 <- lm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude4c2) mMoroccoABWVSwofraude4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude4d) mMoroccoABWVSwofraude4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude4d2) mMoroccoABWVSwofraude4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude4e) mMoroccoABWVSwofraude4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude4e2) mMoroccoABWVSwofraude4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude4f) mMoroccoABWVSwofraude4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = MoroccoABWVSwofraude) summary(mMoroccoABWVSwofraude4f2) ################################################### ##############Jordan: PERCENT MATCH################ ############################AB##################### ################################################### table(dmAB3$country) Jordan <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Jordan") JordanABmat <- as.matrix(Jordan) percent_match_JordanAB <- c() for(i in seq(nrow(JordanABmat))){ vec <- JordanABmat[i,] dat <- JordanABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_JordanAB[i] <- max(diff) } qplot(percent_match_JordanAB) summary(percent_match_JordanAB) JordanAB3 <- as_tibble(JordanABmat) is.numeric(percent_match_JordanAB) JordanAB3$matchover85JordanAB <- rep(NA,1795) JordanAB3$matchover85JordanAB[percent_match_JordanAB <= 0.849] <-0 JordanAB3$matchover85JordanAB[percent_match_JordanAB >= 0.850] <-1 table(JordanAB3$matchover85JordanAB) # 0, 1% prop.table(table(JordanAB3$matchover85JordanAB)) ##########Jordan: PERCENT MATCH################ ###############WVS##################### JordanWVS <- dmWV6 %>% mutate(country = as.factor(V2)) %>% filter(country == "Jordan") JordanWVSmat <- as.matrix(JordanWVS) percent_match_JordanWVS <- c() for(i in seq(nrow(JordanWVSmat))){ vec <- JordanWVSmat[i,] dat <- JordanWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_JordanWVS[i] <- max(diff) } qplot(percent_match_JordanWVS) summary(percent_match_JordanWVS) JordanWVS2 <- as_tibble(JordanWVSmat) JordanWVS2$matchover85JordanWVS <- rep(NA,1198) JordanWVS2$matchover85JordanWVS[percent_match_JordanWVS <= 0.849] <-0 JordanWVS2$matchover85JordanWVS[percent_match_JordanWVS >= 0.850] <-1 table(JordanWVS2$matchover85JordanWVS) # 0% prop.table(table(JordanWVS2$matchover85JordanWVS)) ##########JORDAN: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(JordanWVS2) JordanWVS2$percentmatch <- as.character(JordanWVS2$matchover85JordanWVS) table(JordanWVS2$percentmatch) table(JordanWVS2$gender) #AB. names(JordanAB3) JordanAB3$percentmatch <- as.character(JordanAB3$matchover85JordanAB) table(JordanAB3$percentmatch) table(JordanAB3$gender) JordanABWVS <- merge(JordanWVS2, JordanAB3, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(JordanABWVS$country) table(JordanABWVS$percentmatch) table(JordanABWVS$Surveytype) JordanABWVS$Trust <- as.numeric(JordanABWVS$Trust) table(JordanABWVS$age_short) JordanABWVS$age_short <- as.numeric(JordanABWVS$age_short) table(JordanABWVS$age_short) table(JordanABWVS$Educ) JordanABWVS$Educ <- as.numeric(JordanABWVS$Educ) table(JordanABWVS$Educ) table(JordanABWVS$gender) JordanABWVS$gender <- as.numeric(JordanABWVS$gender) table(JordanABWVS$gender) table(JordanABWVS$Empl) JordanABWVS$Empl <- as.numeric(JordanABWVS$Empl) table(JordanABWVS$Empl) JordanABWVS$Surveytype2[JordanABWVS$Surveytype == 1] <- 0 JordanABWVS$Surveytype2[JordanABWVS$Surveytype == 0] <- 1 table(JordanABWVS$Surveytype2) #########################################JORDAN: PERCENT MATCH################ ###############re-running regressions ALL CASES IN JORDAN TO CHECK##################### mJordanABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = JordanABWVS, family = binomial) summary(mJordanABWVS1a) mJordanABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = JordanABWVS, family = binomial) summary(mJordanABWVS1b) mJordanABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = JordanABWVS, family = binomial) summary(mJordanABWVS1c) mJordanABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = JordanABWVS, family = binomial) summary(mJordanABWVS1c2) mJordanABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS, family = binomial) summary(mJordanABWVS1d) mJordanABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS, family = binomial) summary(mJordanABWVS1e) mJordanABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = JordanABWVS, family = binomial) summary(mJordanABWVS1e2) mJordanABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS, family = binomial) summary(mJordanABWVS1f) mJordanABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = JordanABWVS, family = binomial) summary(mJordanABWVS1f2) table(JordanABWVS$Unieduc) table(JordanABWVS$country) mJordanABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS2a) mJordanABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS2b) mJordanABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS2c) mJordanABWVS2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS2c2) mJordanABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS2d) mJordanABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS2e) mJordanABWVS2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS2e2) mJordanABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS2f) mJordanABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS2f2) table(JordanABWVS$PolLead) table(JordanABWVS$country) mJordanABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS3a) mJordanABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS3b) mJordanABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS3c) mJordanABWVS3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS3c2) mJordanABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS3d) mJordanABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS3e) mJordanABWVS3e2 <- lm(PolLead ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS3e2) mJordanABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS3f) table(JordanABWVS$Trustpol) table(JordanABWVS$country) mJordanABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS4a) mJordanABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS4b) mJordanABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS4c) mJordanABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS4d) mJordanABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS4d2) mJordanABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS4e) mJordanABWVS4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS4e2) mJordanABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS4f) mJordanABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = JordanABWVS) summary(mJordanABWVS4f2) ################################################### #############Lebanon: PERCENT MATCH################ ############################AB##################### ################################################### table(dmAB3$country) Lebanon <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Lebanon") LebanonABmat <- as.matrix(Lebanon) percent_match_LebanonAB <- c() for(i in seq(nrow(LebanonABmat))){ vec <- LebanonABmat[i,] dat <- LebanonABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_LebanonAB[i] <- max(diff) } qplot(percent_match_LebanonAB) summary(percent_match_LebanonAB) LebanonAB3 <- as_tibble(LebanonABmat) is.numeric(percent_match_LebanonAB) LebanonAB3$matchover85LebanonAB <- rep(NA,1198) LebanonAB3$matchover85LebanonAB[percent_match_LebanonAB <= 0.849] <-0 LebanonAB3$matchover85LebanonAB[percent_match_LebanonAB >= 0.850] <-1 table(LebanonAB3$matchover85LebanonAB) # prop.table(table(LebanonAB3$matchover85LebanonAB)) ##########Lebanon: PERCENT MATCH################ ###############WVS##################### LebanonWVS <- dmWV6 %>% mutate(country = as.factor(V2)) %>% filter(country == "Lebanon") LebanonWVSmat <- as.matrix(LebanonWVS) percent_match_LebanonWVS <- c() for(i in seq(nrow(LebanonWVSmat))){ vec <- LebanonWVSmat[i,] dat <- LebanonWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_LebanonWVS[i] <- max(diff) } qplot(percent_match_LebanonWVS) summary(percent_match_LebanonWVS) LebanonWVS2 <- as_tibble(LebanonWVSmat) LebanonWVS2$matchover85LebanonWVS <- rep(NA,1200) LebanonWVS2$matchover85LebanonWVS[percent_match_LebanonWVS <= 0.849] <-0 LebanonWVS2$matchover85LebanonWVS[percent_match_LebanonWVS >= 0.850] <-1 table(LebanonWVS2$matchover85LebanonWVS) # prop.table(table(LebanonWVS2$matchover85LebanonWVS)) ##########LEBANON: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(LebanonWVS2) LebanonWVS2$percentmatch <- as.character(LebanonWVS2$matchover85LebanonWVS) table(LebanonWVS2$percentmatch) table(LebanonWVS2$gender) #AB. names(LebanonAB3) LebanonAB3$percentmatch <- as.character(LebanonAB3$matchover85LebanonAB) table(LebanonAB3$percentmatch) table(LebanonAB3$gender) LebanonABWVS <- merge(LebanonWVS2, LebanonAB3, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(LebanonABWVS$country) table(LebanonABWVS$percentmatch) table(LebanonABWVS$Surveytype) LebanonABWVS$Trust <- as.numeric(LebanonABWVS$Trust) table(LebanonABWVS$age_short) LebanonABWVS$age_short <- as.numeric(LebanonABWVS$age_short) table(LebanonABWVS$age_short) table(LebanonABWVS$Educ) LebanonABWVS$Educ <- as.numeric(LebanonABWVS$Educ) table(LebanonABWVS$Educ) table(LebanonABWVS$gender) LebanonABWVS$gender <- as.numeric(LebanonABWVS$gender) table(LebanonABWVS$gender) table(LebanonABWVS$Empl) LebanonABWVS$Empl <- as.numeric(LebanonABWVS$Empl) table(LebanonABWVS$Empl) LebanonABWVS$Surveytype2[LebanonABWVS$Surveytype == 1] <- 0 LebanonABWVS$Surveytype2[LebanonABWVS$Surveytype == 0] <- 1 table(LebanonABWVS$Surveytype2) #########################################LEBANON: PERCENT MATCH################ ###############re-running regressions ALL CASES IN LEBANON TO CHECK##################### mLebanonABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = LebanonABWVS, family = binomial) summary(mLebanonABWVS1a) mLebanonABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = LebanonABWVS, family = binomial) summary(mLebanonABWVS1b) mLebanonABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = LebanonABWVS, family = binomial) summary(mLebanonABWVS1c) mLebanonABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = LebanonABWVS, family = binomial) summary(mLebanonABWVS1c2) mLebanonABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS, family = binomial) summary(mLebanonABWVS1d) mLebanonABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS, family = binomial) summary(mLebanonABWVS1e) mLebanonABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = LebanonABWVS, family = binomial) summary(mLebanonABWVS1e2) mLebanonABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS, family = binomial) summary(mLebanonABWVS1f) mLebanonABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = LebanonABWVS, family = binomial) summary(mLebanonABWVS1f2) table(LebanonABWVS$Unieduc) table(LebanonABWVS$country) mLebanonABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS2a) mLebanonABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS2b) mLebanonABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS2c) mLebanonABWVS2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS2c2) mLebanonABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS2d) mLebanonABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS2e) mLebanonABWVS2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS2e2) mLebanonABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS2f) mLebanonABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS2f2) table(LebanonABWVS$PolLead) table(LebanonABWVS$country) mLebanonABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS3a) mLebanonABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS3b) mLebanonABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS3c) mLebanonABWVS3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS3c2) mLebanonABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS3d) mLebanonABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS3e) mLebanonABWVS3e2 <- lm(PolLead ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS3e2) mLebanonABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS3f) table(LebanonABWVS$Trustpol) table(LebanonABWVS$country) mLebanonABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS4a) mLebanonABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS4b) mLebanonABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS4c) mLebanonABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS4d) mLebanonABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS4d2) mLebanonABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS4e) mLebanonABWVS4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS4e2) mLebanonABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS4f) mLebanonABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = LebanonABWVS) summary(mLebanonABWVS4f2) ################################################### ##########Libya: PERCENT MATCH################ ######################AB##################### ################################################ table(dmAB3$country) Libya <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Libya") LibyaABmat <- as.matrix(Libya) percent_match_LibyaAB <- c() for(i in seq(nrow(LibyaABmat))){ vec <- LibyaABmat[i,] dat <- LibyaABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_LibyaAB[i] <- max(diff) } qplot(percent_match_LibyaAB) summary(percent_match_LibyaAB) LibyaAB3 <- as_tibble(LibyaABmat) is.numeric(percent_match_LibyaAB) LibyaAB3$matchover85LibyaAB <- rep(NA,1241) LibyaAB3$matchover85LibyaAB[percent_match_LibyaAB <= 0.849] <-0 LibyaAB3$matchover85LibyaAB[percent_match_LibyaAB >= 0.850] <-1 table(LibyaAB3$matchover85LibyaAB) # prop.table(table(LibyaAB3$matchover85LibyaAB)) ##########Libya: PERCENT MATCH################ ###############WVS##################### LibyaWVS <- dmWV6 %>% mutate(country = as.factor(V2)) %>% filter(country == "Libya") LibyaWVSmat <- as.matrix(LibyaWVS) percent_match_LibyaWVS <- c() for(i in seq(nrow(LibyaWVSmat))){ vec <- LibyaWVSmat[i,] dat <- LibyaWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_LibyaWVS[i] <- max(diff) } qplot(percent_match_LibyaWVS) summary(percent_match_LibyaWVS) LibyaWVS2 <- as_tibble(LibyaWVSmat) LibyaWVS2$matchover85LibyaWVS <- rep(NA,2131) LibyaWVS2$matchover85LibyaWVS[percent_match_LibyaWVS <= 0.849] <-0 LibyaWVS2$matchover85LibyaWVS[percent_match_LibyaWVS >= 0.850] <-1 table(LibyaWVS2$matchover85LibyaWVS) # prop.table(table(LibyaWVS2$matchover85LibyaWVS)) ##########LIBYA: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(LibyaWVS2) LibyaWVS2$percentmatch <- as.character(LibyaWVS2$matchover85LibyaWVS) table(LibyaWVS2$percentmatch) table(LibyaWVS2$gender) #AB. names(LibyaAB3) LibyaAB3$percentmatch <- as.character(LibyaAB3$matchover85LibyaAB) table(LibyaAB3$percentmatch) table(LibyaAB3$gender) LibyaABWVS <- merge(LibyaWVS2, LibyaAB3, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(LibyaABWVS$country) table(LibyaABWVS$percentmatch) table(LibyaABWVS$Surveytype) LibyaABWVS$Trust <- as.numeric(LibyaABWVS$Trust) table(LibyaABWVS$age_short) LibyaABWVS$age_short <- as.numeric(LibyaABWVS$age_short) table(LibyaABWVS$age_short) table(LibyaABWVS$Educ) LibyaABWVS$Educ <- as.numeric(LibyaABWVS$Educ) table(LibyaABWVS$Educ) table(LibyaABWVS$gender) LibyaABWVS$gender <- as.numeric(LibyaABWVS$gender) table(LibyaABWVS$gender) table(LibyaABWVS$Empl) LibyaABWVS$Empl <- as.numeric(LibyaABWVS$Empl) table(LibyaABWVS$Empl) LibyaABWVS$Surveytype2[LibyaABWVS$Surveytype == 1] <- 0 LibyaABWVS$Surveytype2[LibyaABWVS$Surveytype == 0] <- 1 table(LibyaABWVS$Surveytype2) #########################################LIBYA: PERCENT MATCH################ ###############re-running regressions ALL CASES IN LIBYA TO CHECK##################### mLibyaABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = LibyaABWVS, family = binomial) summary(mLibyaABWVS1a) mLibyaABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = LibyaABWVS, family = binomial) summary(mLibyaABWVS1b) mLibyaABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = LibyaABWVS, family = binomial) summary(mLibyaABWVS1c) mLibyaABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = LibyaABWVS, family = binomial) summary(mLibyaABWVS1c2) mLibyaABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS, family = binomial) summary(mLibyaABWVS1d) mLibyaABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS, family = binomial) summary(mLibyaABWVS1e) mLibyaABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = LibyaABWVS, family = binomial) summary(mLibyaABWVS1e2) mLibyaABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS, family = binomial) summary(mLibyaABWVS1f) mLibyaABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = LibyaABWVS, family = binomial) summary(mLibyaABWVS1f2) table(LibyaABWVS$Unieduc) table(LibyaABWVS$country) mLibyaABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS2a) mLibyaABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS2b) mLibyaABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS2c) mLibyaABWVS2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS2c2) mLibyaABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS2d) mLibyaABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS2e) mLibyaABWVS2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS2e2) mLibyaABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS2f) mLibyaABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS2f2) table(LibyaABWVS$PolLead) table(LibyaABWVS$country) mLibyaABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS3a) mLibyaABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS3b) mLibyaABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS3c) mLibyaABWVS3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS3c2) mLibyaABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS3d) mLibyaABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS3e) mLibyaABWVS3e2 <- lm(PolLead ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS3e2) mLibyaABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS3f) table(LibyaABWVS$Trustpol) table(LibyaABWVS$country) mLibyaABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS4a) mLibyaABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS4b) mLibyaABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS4c) mLibyaABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS4d) mLibyaABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS4d2) mLibyaABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS4e) mLibyaABWVS4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS4e2) mLibyaABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS4f) mLibyaABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = LibyaABWVS) summary(mLibyaABWVS4f2) ################################################### ##########Palestine: PERCENT MATCH################ ######################AB##################### ################################################ table(dmAB3$country) Palestine <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Palestine") PalestineABmat <- as.matrix(Palestine) percent_match_PalestineAB <- c() for(i in seq(nrow(PalestineABmat))){ vec <- PalestineABmat[i,] dat <- PalestineABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_PalestineAB[i] <- max(diff) } qplot(percent_match_PalestineAB) summary(percent_match_PalestineAB) PalestineAB3 <- as_tibble(PalestineABmat) is.numeric(percent_match_PalestineAB) PalestineAB3$matchover85PalestineAB <- rep(NA,1194) PalestineAB3$matchover85PalestineAB[percent_match_PalestineAB <= 0.849] <-0 PalestineAB3$matchover85PalestineAB[percent_match_PalestineAB >= 0.850] <-1 table(PalestineAB3$matchover85PalestineAB) # prop.table(table(PalestineAB3$matchover85PalestineAB)) ##########Palestine: PERCENT MATCH################ ###############WVS##################### PalestineWVS <- dmWV6 %>% mutate(country = as.factor(V2)) %>% filter(country == "Palestine") PalestineWVSmat <- as.matrix(PalestineWVS) percent_match_PalestineWVS <- c() for(i in seq(nrow(PalestineWVSmat))){ vec <- PalestineWVSmat[i,] dat <- PalestineWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_PalestineWVS[i] <- max(diff) } qplot(percent_match_PalestineWVS) summary(percent_match_PalestineWVS) PalestineWVS2 <- as_tibble(PalestineWVSmat) PalestineWVS2$matchover85PalestineWVS <- rep(NA,998) PalestineWVS2$matchover85PalestineWVS[percent_match_PalestineWVS <= 0.849] <-0 PalestineWVS2$matchover85PalestineWVS[percent_match_PalestineWVS >= 0.850] <-1 table(PalestineWVS2$matchover85PalestineWVS) # prop.table(table(PalestineWVS2$matchover85PalestineWVS)) ##########PALESTINE: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(PalestineWVS2) PalestineWVS2$percentmatch <- as.character(PalestineWVS2$matchover85PalestineWVS) table(PalestineWVS2$percentmatch) table(PalestineWVS2$gender) #AB. names(PalestineAB3) PalestineAB3$percentmatch <- as.character(PalestineAB3$matchover85PalestineAB) table(PalestineAB3$percentmatch) table(PalestineAB3$gender) PalestineABWVS <- merge(PalestineWVS2, PalestineAB3, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(PalestineABWVS$country) table(PalestineABWVS$percentmatch) table(PalestineABWVS$Surveytype) PalestineABWVS$Trust <- as.numeric(PalestineABWVS$Trust) table(PalestineABWVS$age_short) PalestineABWVS$age_short <- as.numeric(PalestineABWVS$age_short) table(PalestineABWVS$age_short) table(PalestineABWVS$Educ) PalestineABWVS$Educ <- as.numeric(PalestineABWVS$Educ) table(PalestineABWVS$Educ) table(PalestineABWVS$gender) PalestineABWVS$gender <- as.numeric(PalestineABWVS$gender) table(PalestineABWVS$gender) table(PalestineABWVS$Empl) PalestineABWVS$Empl <- as.numeric(PalestineABWVS$Empl) table(PalestineABWVS$Empl) PalestineABWVS$Surveytype2[PalestineABWVS$Surveytype == 1] <- 0 PalestineABWVS$Surveytype2[PalestineABWVS$Surveytype == 0] <- 1 table(PalestineABWVS$Surveytype2) #########################################PALESTINE: PERCENT MATCH################ ###############re-running regressions ALL CASES IN PALESTINE TO CHECK##################### mPalestineABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = PalestineABWVS, family = binomial) summary(mPalestineABWVS1a) mPalestineABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = PalestineABWVS, family = binomial) summary(mPalestineABWVS1b) mPalestineABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = PalestineABWVS, family = binomial) summary(mPalestineABWVS1c) mPalestineABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = PalestineABWVS, family = binomial) summary(mPalestineABWVS1c2) mPalestineABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS, family = binomial) summary(mPalestineABWVS1d) mPalestineABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS, family = binomial) summary(mPalestineABWVS1e) mPalestineABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = PalestineABWVS, family = binomial) summary(mPalestineABWVS1e2) mPalestineABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS, family = binomial) summary(mPalestineABWVS1f) mPalestineABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = PalestineABWVS, family = binomial) summary(mPalestineABWVS1f2) table(PalestineABWVS$Unieduc) table(PalestineABWVS$country) mPalestineABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS2a) mPalestineABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS2b) mPalestineABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS2c) mPalestineABWVS2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS2c2) mPalestineABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS2d) mPalestineABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS2e) mPalestineABWVS2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS2e2) mPalestineABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS2f) mPalestineABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS2f2) table(PalestineABWVS$PolLead) table(PalestineABWVS$country) mPalestineABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS3a) mPalestineABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS3b) mPalestineABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS3c) mPalestineABWVS3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS3c2) mPalestineABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS3d) mPalestineABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS3e) mPalestineABWVS3e2 <- lm(PolLead ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS3e2) mPalestineABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS3f) table(PalestineABWVS$Trustpol) table(PalestineABWVS$country) mPalestineABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS4a) mPalestineABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS4b) mPalestineABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS4c) mPalestineABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS4d) mPalestineABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS4d2) mPalestineABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS4e) mPalestineABWVS4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS4e2) mPalestineABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS4f) mPalestineABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = PalestineABWVS) summary(mPalestineABWVS4f2) ################################################### #############Tunisia: PERCENT MATCH################ ############################AB##################### ################################################### table(dmAB3$country) Tunisia <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Tunisia") TunisiaABmat <- as.matrix(Tunisia) percent_match_TunisiaAB <- c() for(i in seq(nrow(TunisiaABmat))){ vec <- TunisiaABmat[i,] dat <- TunisiaABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_TunisiaAB[i] <- max(diff) } qplot(percent_match_TunisiaAB) summary(percent_match_TunisiaAB) TunisiaAB3 <- as_tibble(TunisiaABmat) is.numeric(percent_match_TunisiaAB) TunisiaAB3$matchover85TunisiaAB <- rep(NA,1191) TunisiaAB3$matchover85TunisiaAB[percent_match_TunisiaAB <= 0.849] <-0 TunisiaAB3$matchover85TunisiaAB[percent_match_TunisiaAB >= 0.850] <-1 table(TunisiaAB3$matchover85TunisiaAB) # prop.table(table(TunisiaAB3$matchover85TunisiaAB)) ##########Tunisia: PERCENT MATCH################ ###############WVS##################### TunisiaWVS <- dmWV6 %>% mutate(country = as.factor(V2)) %>% filter(country == "Tunisia") TunisiaWVSmat <- as.matrix(TunisiaWVS) percent_match_TunisiaWVS <- c() for(i in seq(nrow(TunisiaWVSmat))){ vec <- TunisiaWVSmat[i,] dat <- TunisiaWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_TunisiaWVS[i] <- max(diff) } qplot(percent_match_TunisiaWVS) summary(percent_match_TunisiaWVS) TunisiaWVS2 <- as_tibble(TunisiaWVSmat) TunisiaWVS2$matchover85TunisiaWVS <- rep(NA,1204) TunisiaWVS2$matchover85TunisiaWVS[percent_match_TunisiaWVS <= 0.849] <-0 TunisiaWVS2$matchover85TunisiaWVS[percent_match_TunisiaWVS >= 0.850] <-1 table(TunisiaWVS2$matchover85TunisiaWVS) # prop.table(table(TunisiaWVS2$matchover85TunisiaWVS)) ##########TUNISIA: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(TunisiaWVS2) TunisiaWVS2$percentmatch <- as.character(TunisiaWVS2$matchover85TunisiaWVS) table(TunisiaWVS2$percentmatch) table(TunisiaWVS2$gender) #AB. names(TunisiaAB3) TunisiaAB3$percentmatch <- as.character(TunisiaAB3$matchover85TunisiaAB) table(TunisiaAB3$percentmatch) table(TunisiaAB3$gender) TunisiaABWVS <- merge(TunisiaWVS2, TunisiaAB3, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(TunisiaABWVS$country) table(TunisiaABWVS$percentmatch) table(TunisiaABWVS$Surveytype) TunisiaABWVS$Trust <- as.numeric(TunisiaABWVS$Trust) table(TunisiaABWVS$age_short) TunisiaABWVS$age_short <- as.numeric(TunisiaABWVS$age_short) table(TunisiaABWVS$age_short) table(TunisiaABWVS$Educ) TunisiaABWVS$Educ <- as.numeric(TunisiaABWVS$Educ) table(TunisiaABWVS$Educ) table(TunisiaABWVS$gender) TunisiaABWVS$gender <- as.numeric(TunisiaABWVS$gender) table(TunisiaABWVS$gender) table(TunisiaABWVS$Empl) TunisiaABWVS$Empl <- as.numeric(TunisiaABWVS$Empl) table(TunisiaABWVS$Empl) TunisiaABWVS$Surveytype2[TunisiaABWVS$Surveytype == 1] <- 0 TunisiaABWVS$Surveytype2[TunisiaABWVS$Surveytype == 0] <- 1 table(TunisiaABWVS$Surveytype2) #########################################TUNISIA: PERCENT MATCH################ ###############re-running regressions ALL CASES IN TUNISIA TO CHECK##################### mTunisiaABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = TunisiaABWVS, family = binomial) summary(mTunisiaABWVS1a) mTunisiaABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = TunisiaABWVS, family = binomial) summary(mTunisiaABWVS1b) mTunisiaABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = TunisiaABWVS, family = binomial) summary(mTunisiaABWVS1c) mTunisiaABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = TunisiaABWVS, family = binomial) summary(mTunisiaABWVS1c2) mTunisiaABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS, family = binomial) summary(mTunisiaABWVS1d) mTunisiaABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS, family = binomial) summary(mTunisiaABWVS1e) mTunisiaABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = TunisiaABWVS, family = binomial) summary(mTunisiaABWVS1e2) mTunisiaABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS, family = binomial) summary(mTunisiaABWVS1f) mTunisiaABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = TunisiaABWVS, family = binomial) summary(mTunisiaABWVS1f2) table(TunisiaABWVS$Unieduc) table(TunisiaABWVS$country) mTunisiaABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS2a) mTunisiaABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS2b) mTunisiaABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS2c) mTunisiaABWVS2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS2c2) mTunisiaABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS2d) mTunisiaABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS2e) mTunisiaABWVS2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS2e2) mTunisiaABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS2f) mTunisiaABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS2f2) table(TunisiaABWVS$PolLead) table(TunisiaABWVS$country) mTunisiaABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS3a) mTunisiaABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS3b) mTunisiaABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS3c) mTunisiaABWVS3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS3c2) mTunisiaABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS3d) mTunisiaABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS3e) mTunisiaABWVS3e2 <- lm(PolLead ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS3e2) mTunisiaABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS3f) table(TunisiaABWVS$Trustpol) table(TunisiaABWVS$country) mTunisiaABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS4a) mTunisiaABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS4b) mTunisiaABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS4c) mTunisiaABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS4d) mTunisiaABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS4d2) mTunisiaABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS4e) mTunisiaABWVS4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS4e2) mTunisiaABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS4f) mTunisiaABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = TunisiaABWVS) summary(mTunisiaABWVS4f2) ################################################### ##########Yemen: PERCENT MATCH################ ######################AB##################### ################################################ table(dmAB3$country) Yemen <- dmAB3 %>% mutate(country = as.factor(country)) %>% filter(country == "Yemen") YemenABmat <- as.matrix(Yemen) percent_match_YemenAB <- c() for(i in seq(nrow(YemenABmat))){ vec <- YemenABmat[i,] dat <- YemenABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_YemenAB[i] <- max(diff) } qplot(percent_match_YemenAB) summary(percent_match_YemenAB) YemenAB3 <- as_tibble(YemenABmat) is.numeric(percent_match_YemenAB) YemenAB3$matchover85YemenAB <- rep(NA,1200) YemenAB3$matchover85YemenAB[percent_match_YemenAB <= 0.849] <-0 YemenAB3$matchover85YemenAB[percent_match_YemenAB >= 0.850] <-1 table(YemenAB3$matchover85YemenAB) # prop.table(table(YemenAB3$matchover85YemenAB)) ##########Yemen: PERCENT MATCH################ ###############WVS##################### YemenWVS <- dmWV6 %>% mutate(country = as.factor(V2)) %>% filter(country == "Yemen") YemenWVSmat <- as.matrix(YemenWVS) percent_match_YemenWVS <- c() for(i in seq(nrow(YemenWVSmat))){ vec <- YemenWVSmat[i,] dat <- YemenWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_YemenWVS[i] <- max(diff) } qplot(percent_match_YemenWVS) summary(percent_match_YemenWVS) YemenWVS2 <- as_tibble(YemenWVSmat) YemenWVS2$matchover85YemenWVS <- rep(NA,998) YemenWVS2$matchover85YemenWVS[percent_match_YemenWVS <= 0.849] <-0 YemenWVS2$matchover85YemenWVS[percent_match_YemenWVS >= 0.850] <-1 table(YemenWVS2$matchover85YemenWVS) prop.table(table(YemenWVS2$matchover85YemenWVS)) ##########YEMEN: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(YemenWVS2) YemenWVS2$percentmatch <- as.character(YemenWVS2$matchover85YemenWVS) table(YemenWVS2$percentmatch) table(YemenWVS2$gender) #AB. names(YemenAB3) YemenAB3$percentmatch <- as.character(YemenAB3$matchover85YemenAB) table(YemenAB3$percentmatch) table(YemenAB3$gender) YemenABWVS <- merge(YemenWVS2, YemenAB3, by= c("Surveytype", "Survey", "Empl", "Mar", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id", "percentmatch"), all=TRUE) table(YemenABWVS$country) table(YemenABWVS$percentmatch) table(YemenABWVS$Surveytype) YemenABWVS$Trust <- as.numeric(YemenABWVS$Trust) table(YemenABWVS$age_short) YemenABWVS$age_short <- as.numeric(YemenABWVS$age_short) table(YemenABWVS$age_short) table(YemenABWVS$Educ) YemenABWVS$Educ <- as.numeric(YemenABWVS$Educ) table(YemenABWVS$Educ) table(YemenABWVS$gender) YemenABWVS$gender <- as.numeric(YemenABWVS$gender) table(YemenABWVS$gender) table(YemenABWVS$Empl) YemenABWVS$Empl <- as.numeric(YemenABWVS$Empl) table(YemenABWVS$Empl) YemenABWVS$Surveytype2[YemenABWVS$Surveytype == 1] <- 0 YemenABWVS$Surveytype2[YemenABWVS$Surveytype == 0] <- 1 table(YemenABWVS$Surveytype2) #########################################YEMEN: PERCENT MATCH################ ###############re-running regressions ALL CASES IN YEMEN TO CHECK##################### mYemenABWVS1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = YemenABWVS, family = binomial) summary(mYemenABWVS1a) mYemenABWVS1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = YemenABWVS, family = binomial) summary(mYemenABWVS1b) mYemenABWVS1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = YemenABWVS, family = binomial) summary(mYemenABWVS1c) mYemenABWVS1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = YemenABWVS, family = binomial) summary(mYemenABWVS1c2) mYemenABWVS1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS, family = binomial) summary(mYemenABWVS1d) mYemenABWVS1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS, family = binomial) summary(mYemenABWVS1e) mYemenABWVS1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVS, family = binomial) summary(mYemenABWVS1e2) mYemenABWVS1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS, family = binomial) summary(mYemenABWVS1f) mYemenABWVS1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVS, family = binomial) summary(mYemenABWVS1f2) table(YemenABWVS$Unieduc) table(YemenABWVS$country) mYemenABWVS2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS2a) mYemenABWVS2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS2b) mYemenABWVS2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS2c) mYemenABWVS2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS2c2) mYemenABWVS2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS2d) mYemenABWVS2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS2e) mYemenABWVS2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS2e2) mYemenABWVS2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS2f) mYemenABWVS2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS2f2) table(YemenABWVS$PolLead) table(YemenABWVS$country) mYemenABWVS3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS3a) mYemenABWVS3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS3b) mYemenABWVS3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS3c) mYemenABWVS3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS3c2) mYemenABWVS3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS3d) mYemenABWVS3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS3e) mYemenABWVS3e2 <- lm(PolLead ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS3e2) mYemenABWVS3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS3f) table(YemenABWVS$Trustpol) table(YemenABWVS$country) mYemenABWVS4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS4a) mYemenABWVS4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS4b) mYemenABWVS4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS4c) mYemenABWVS4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS4d) mYemenABWVS4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS4d2) mYemenABWVS4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS4e) mYemenABWVS4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS4e2) mYemenABWVS4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS4f) mYemenABWVS4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVS) summary(mYemenABWVS4f2) #########################################YEMEN: PERCENT MATCH################ ###############regressions WITHOUT FRAUDULENT CASES##################### table(YemenABWVS$country) table(YemenABWVS$percentmatch) YemenABWVSwofraude <- subset(YemenABWVS, percentmatch!= "1") table(YemenABWVSwofraude$percentmatch) table(YemenABWVSwofraude$country) table(YemenABWVS$Surveytype) table(YemenABWVS$gender) mYemenABWVSwofraude1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude, family = binomial) summary(mYemenABWVSwofraude1a) mYemenABWVSwofraude1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl , data = YemenABWVSwofraude, family = binomial) summary(mYemenABWVSwofraude1b) mYemenABWVSwofraude1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl , data = YemenABWVSwofraude, family = binomial) summary(mYemenABWVSwofraude1c) mYemenABWVSwofraude1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl , data = YemenABWVSwofraude, family = binomial) summary(mYemenABWVSwofraude1c2) mYemenABWVSwofraude1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude, family = binomial) summary(mYemenABWVSwofraude1d) mYemenABWVSwofraude1e <- glm(Trust ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude, family = binomial) summary(mYemenABWVSwofraude1e) mYemenABWVSwofraude1e2 <- glm(Trust ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude, family = binomial) summary(mYemenABWVSwofraude1e2) mYemenABWVSwofraude1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude, family = binomial) summary(mYemenABWVSwofraude1f) mYemenABWVSwofraude1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude, family = binomial) summary(mYemenABWVSwofraude1f2) table(YemenABWVSwofraude$Unieduc) table(YemenABWVSwofraude$country) mYemenABWVSwofraude2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude2a) mYemenABWVSwofraude2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude2b) mYemenABWVSwofraude2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude2c) mYemenABWVSwofraude2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude2c2) mYemenABWVSwofraude2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude2d) mYemenABWVSwofraude2e <- lm(Unieduc ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude2e) mYemenABWVSwofraude2e2 <- lm(Unieduc ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude2e2) mYemenABWVSwofraude2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude2f) mYemenABWVSwofraude2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude2f2) table(YemenABWVSwofraude$PolLead) table(YemenABWVSwofraude$country) mYemenABWVSwofraude3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude3a) mYemenABWVSwofraude3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude3b) mYemenABWVSwofraude3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude3c) mYemenABWVSwofraude3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude3c2) mYemenABWVSwofraude3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude3d) mYemenABWVSwofraude3d2 <- lm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude3d2) mYemenABWVSwofraude3e <- lm(PolLead ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude3e) mYemenABWVSwofraude3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude3f) table(YemenABWVSwofraude$Trustpol) table(YemenABWVSwofraude$country) mYemenABWVSwofraude4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude4a) mYemenABWVSwofraude4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude4b) mYemenABWVSwofraude4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude4c) mYemenABWVSwofraude4c2 <- lm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude4c2) mYemenABWVSwofraude4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude4d) mYemenABWVSwofraude4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude4d2) mYemenABWVSwofraude4e <- lm(Trustpol ~ Surveytype + gender + Mar*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude4e) mYemenABWVSwofraude4e2 <- lm(Trustpol ~ Surveytype2 + gender + Mar*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude4e2) mYemenABWVSwofraude4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude4f) mYemenABWVSwofraude4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + Mar + Empl, data = YemenABWVSwofraude) summary(mYemenABWVSwofraude4f2) ################################################################################################################## ################################################################################################################## #########################################PERCENT MATCH ANALYSES 2018############################################## ################################################################################################################## ################################################################################################################## ################################################ #################ID############################# ################################################ table(dmAB5$Survey) dmAB5$Survey <- rep("AB", 12061) table(dmWV7$Survey) dmWV7$Survey <- rep("WVS", 6011) dmAB5$id <- c(1:12061) summary(dmAB5$id) length(dmWV7$Survey) dmWV7$id <- c(12062:18072) summary(dmWV7$id) # Merging AB and WVS, all variables, full datasets--------------------------------------------------- table(dmWV7$id) ABWVS <- merge(dmAB5, dmWV7, by= c("Surveytype", "Survey", "Empl", "Mar", "singled", "othermaritald", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "Trustpol", "id"), all=TRUE) names(ABWVS) summary(ABWVS$id) table(ABWVS$Empl) table(ABWVS$survey) table(ABWVS$country) table(ABWVS$singled) summary(ABWVS$Surveytype) ABWVS$Surveytype <- as.factor (ABWVS$Surveytype) summary(ABWVS$Surveytype) table(ABWVS$Surveytype) ABWVS$Surveytype2[ABWVS$Surveytype == 1] <- 0 ABWVS$Surveytype2[ABWVS$Surveytype == 0] <- 1 table(ABWVS$Surveytype2) #Surveytype has 0 WVS, 1 AB // Surveytype2 has 0 AB, 1 WVS ################################################################################################################## ################################################################################################################## #####################################ANALYSIS 2: PER-COUNTRY DESCRIPTIVES######################################### ###################################IN EXCEL FILE: "UNIVARIATE ANALYSES PT 1"###################################### ################################################################################################################## ################################################################################################################## ################################################ ######################EGYPT##################### ################################################ table(ABWVS$country) Egypt <- ABWVS[ABWVS$country == "Egypt", ] table(Egypt$country) summary(Egypt$Surveytype) aggregate(Egypt$Trust ~ Surveytype, Egypt, mean) chisq.test(table(Egypt$Trust,Egypt$Surveytype)) t.test(Egypt$Unieduc ~ Egypt$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Egypt$PolLead ~ Egypt$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) #t.test(Egypt$Trustpol ~ Egypt$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(Egypt$Trust, Egypt$Surveytype), 2) prop.table (table(Egypt$Unieduc, Egypt$Surveytype), 2) prop.table (table(Egypt$PolLead, Egypt$Surveytype), 2) # prop.table (table(Egypt$Trustpol, Egypt$Surveytype), 2) aggregate(Egypt$Trust ~ Surveytype, Egypt, length) aggregate(Egypt$Unieduc ~ Surveytype, Egypt, length) aggregate(Egypt$PolLead ~ Surveytype, Egypt, length) #aggregate(Egypt$Trustpol ~ Surveytype, Egypt, length) ################################################ ####################IRAQ######################## ################################################ table(ABWVS$country) Iraq <- ABWVS[ABWVS$country == "Iraq", ] table(Iraq$country) summary(Iraq$Surveytype) aggregate(Iraq$Trust ~ Surveytype, Iraq, mean) chisq.test(table(Iraq$Trust,Iraq$Surveytype)) t.test(Iraq$Unieduc ~ Iraq$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Iraq$PolLead ~ Iraq$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Iraq$Trustpol ~ Iraq$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(Iraq$Trust, Iraq$Surveytype), 2) prop.table (table(Iraq$Unieduc, Iraq$Surveytype), 2) prop.table (table(Iraq$PolLead, Iraq$Surveytype), 2) prop.table (table(Iraq$Trustpol, Iraq$Surveytype), 2) aggregate(Iraq$Trust ~ Surveytype, Iraq, length) aggregate(Iraq$Unieduc ~ Surveytype, Iraq, length) aggregate(Iraq$PolLead ~ Surveytype, Iraq, length) aggregate(Iraq$Trustpol ~ Surveytype, Iraq, length) ################################################ ####################JORDAN###################### ################################################ table(ABWVS$country) Jordan <- ABWVS[ABWVS$country == "Jordan", ] table(Jordan$country) table(ABWVS$country) summary(Jordan$Surveytype) aggregate(Jordan$Trust ~ Surveytype, Jordan, mean) chisq.test(table(Jordan$Trust,Jordan$Surveytype)) t.test(Jordan$Unieduc ~ Jordan$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Jordan$PolLead ~ Jordan$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Jordan$Trustpol ~ Jordan$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(Jordan$Trust, Jordan$Surveytype), 2) prop.table (table(Jordan$Unieduc, Jordan$Surveytype), 2) prop.table (table(Jordan$PolLead, Jordan$Surveytype), 2) prop.table (table(Jordan$Trustpol, Jordan$Surveytype), 2) aggregate(Jordan$Trust ~ Surveytype, Jordan, length) aggregate(Jordan$Unieduc ~ Surveytype, Jordan, length) aggregate(Jordan$PolLead ~ Surveytype, Jordan, length) aggregate(Jordan$Trustpol ~ Surveytype, Jordan, length) ################################################ ####################LEBANON##################### ################################################ table(ABWVS$country) Lebanon <- ABWVS[ABWVS$country == "Lebanon", ] table(Lebanon$country) table(ABWVS$country) summary(Lebanon$Surveytype) aggregate(Lebanon$Trust ~ Surveytype, Lebanon, mean) chisq.test(table(Lebanon$Trust,Lebanon$Surveytype)) t.test(Lebanon$Unieduc ~ Lebanon$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Lebanon$PolLead ~ Lebanon$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Lebanon$Trustpol ~ Lebanon$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(Lebanon$Trust, Lebanon$Surveytype), 2) prop.table (table(Lebanon$Unieduc, Lebanon$Surveytype), 2) prop.table (table(Lebanon$PolLead, Lebanon$Surveytype), 2) prop.table (table(Lebanon$Trustpol, Lebanon$Surveytype), 2) aggregate(Lebanon$Trust ~ Surveytype, Lebanon, length) aggregate(Lebanon$Unieduc ~ Surveytype, Lebanon, length) aggregate(Lebanon$PolLead ~ Surveytype, Lebanon, length) aggregate(Lebanon$Trustpol ~ Surveytype, Lebanon, length) ################################################ ####################TUNISIA##################### ################################################ table(ABWVS$country) Tunisia <- ABWVS[ABWVS$country == "Tunisia", ] table(Tunisia$country) table(ABWVS$country) summary(Tunisia$Surveytype) aggregate(Tunisia$Trust ~ Surveytype, Tunisia, mean) chisq.test(table(Tunisia$Trust,Tunisia$Surveytype)) t.test(Tunisia$Unieduc ~ Tunisia$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Tunisia$PolLead ~ Tunisia$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) t.test(Tunisia$Trustpol ~ Tunisia$Surveytype, mu=0, alt = "two.sided", conf=0.95, var.eq=F, paired=F) prop.table (table(Tunisia$Trust, Tunisia$Surveytype), 2) prop.table (table(Tunisia$Unieduc, Tunisia$Surveytype), 2) prop.table (table(Tunisia$PolLead, Tunisia$Surveytype), 2) prop.table (table(Tunisia$Trustpol, Tunisia$Surveytype), 2) aggregate(Tunisia$Trust ~ Surveytype, Tunisia, length) aggregate(Tunisia$Unieduc ~ Surveytype, Tunisia, length) aggregate(Tunisia$PolLead ~ Surveytype, Tunisia, length) aggregate(Tunisia$Trustpol ~ Surveytype, Tunisia, length) ################################################################################################################## ################################################################################################################## ##############################################PERCENT MATCH####################################################### ################################################################################################################## ################################################################################################################## getwd() #setwd("C:/R"). library(tidyverse) library(Matrix) library(lme4) library(foreign) library(ggplot2) detach("package:memisc", unload = TRUE) ################################################### ##############EGYPT: PERCENT MATCH################# ###########################AB###################### ################################################### table(dmAB5$country) Egypt <- dmAB5 %>% mutate(country = as.factor(country)) %>% filter(country == "Egypt") table(Egypt$Surveytype) EgyptABmat <- as.matrix(Egypt) percent_match_EgyptAB <- c() for(i in seq(nrow(EgyptABmat))){ vec <- EgyptABmat[i,] dat <- EgyptABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_EgyptAB[i] <- max(diff) } qplot(percent_match_EgyptAB) summary(percent_match_EgyptAB) EgyptAB3 <- as_tibble(EgyptABmat) is.numeric(percent_match_EgyptAB) EgyptAB3$matchover85EgyptAB <- rep(NA,2400) EgyptAB3$matchover85EgyptAB[percent_match_EgyptAB <= 0.849] <-0 EgyptAB3$matchover85EgyptAB[percent_match_EgyptAB >= 0.850] <-1 table(EgyptAB3$matchover85EgyptAB) # 2 cases. prop.table(table(EgyptAB3$matchover85EgyptAB)) table(EgyptAB3$singled) ##########EGYPT: PERCENT MATCH################ ###############WVS##################### EgyptWVS <- dmWV7 %>% mutate(country = as.factor(B_COUNTRY)) %>% filter(country == "Egypt") table(EgyptWVS$country) EgyptWVSmat <- as.matrix(EgyptWVS) percent_match_EgyptWVS <- c() for(i in seq(nrow(EgyptWVSmat))){ vec <- EgyptWVSmat[i,] dat <- EgyptWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_EgyptWVS[i] <- max(diff) } qplot(percent_match_EgyptWVS) summary(percent_match_EgyptWVS) EgyptWVS2 <- as_tibble(EgyptWVSmat) EgyptWVS2$matchover85EgyptWVS <- rep(NA,1200) EgyptWVS2$matchover85EgyptWVS[percent_match_EgyptWVS <= 0.849] <-0 EgyptWVS2$matchover85EgyptWVS[percent_match_EgyptWVS >= 0.850] <-1 table(EgyptWVS2$matchover85EgyptWVS) # 84 cases, 0.07. prop.table(table(EgyptWVS2$matchover85EgyptWVS)) table(EgyptWVS2$singled) table(EgyptAB3$gender) ##########EGYPT: PERCENT MATCH################ ###############creating percent match var and merging and deselecting##################### #WVS. names(EgyptWVS2) EgyptWVS2$percentmatch <- as.character(EgyptWVS2$matchover85EgyptWVS) table(EgyptWVS2$percentmatch) table(EgyptWVS2$gender) #AB. names(EgyptAB3) EgyptAB3$percentmatch <- as.character(EgyptAB3$matchover85EgyptAB) table(EgyptAB3$percentmatch) table(EgyptAB3$gender) EgyptABWVS <- merge(EgyptWVS2, EgyptAB3, by= c("Surveytype", "Empl", "Mar", "singled", "othermaritald", "Educ", "age_short", "gender", "country", "Trust", "Unieduc", "PolLead", "id", "percentmatch"), all=TRUE) table(EgyptWVS2$percentmatch) table(EgyptAB3$id) table(EgyptABWVS$country) table(EgyptABWVS$singled) table(EgyptABWVS$percentmatch) table(EgyptABWVS$Surveytype) table(EgyptABWVS$gender) table(EgyptABWVS$Mar) table(EgyptABWVS$Trust) EgyptABWVS$Trust <- as.numeric(EgyptABWVS$Trust) table(EgyptABWVS$age_short) EgyptABWVS$age_short <- as.numeric(EgyptABWVS$age_short) table(EgyptABWVS$age_short) table(EgyptABWVS$Educ) EgyptABWVS$Educ <- as.numeric(EgyptABWVS$Educ) table(EgyptABWVS$Educ) EgyptABWVS$Mar <- as.numeric(EgyptABWVS$Mar) table(EgyptABWVS$Mar) EgyptABWVS$singled <- as.numeric(EgyptABWVS$singled) table(EgyptABWVS$singled) EgyptABWVS$othermaritald <- as.numeric(EgyptABWVS$othermaritald) table(EgyptABWVS$othermaritald) EgyptABWVS$Surveytype2[EgyptABWVS$Surveytype == 1] <- 0 EgyptABWVS$Surveytype2[EgyptABWVS$Surveytype == 0] <- 1 table(EgyptABWVS$Surveytype2) ################################################ #########EGYPTABWVSWOFRAUDE##################### ################################################ ############UNIVARIATES######################### table(EgyptABWVS$country) table(EgyptABWVS$percentmatch) EgyptABWVSwofraude <- subset(EgyptABWVS, percentmatch!= "1") table(EgyptABWVSwofraude$percentmatch) table(EgyptABWVSwofraude$country) table(EgyptABWVSwofraude$gender) prop.table (table(EgyptABWVSwofraude$Trust, EgyptABWVSwofraude$Surveytype), 2) prop.table (table(EgyptABWVSwofraude$Unieduc, EgyptABWVSwofraude$Surveytype), 2) prop.table (table(EgyptABWVSwofraude$PolLead, EgyptABWVSwofraude$Surveytype), 2) #prop.table (table(EgyptABWVSwofraude$Trustpol, EgyptABWVSwofraude$Surveytype), 2) table(Egypt$Trustpol, Egypt$Surveytype) #########################################EGYPT: PERCENT MATCH################ ###############regressions WITHOUT FRAUDULENT CASES##################### mEgyptABWVSwofraude1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1a) mEgyptABWVSwofraude1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1b) mEgyptABWVSwofraude1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1c) mEgyptABWVSwofraude1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl , data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1c2) mEgyptABWVSwofraude1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1d) mEgyptABWVSwofraude1e <- glm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1e) mEgyptABWVSwofraude1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude, family = binomial) summary(mEgyptABWVSwofraude1f) table(EgyptABWVSwofraude$Unieduc) table(EgyptABWVSwofraude$country) mEgyptABWVSwofraude2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2a) mEgyptABWVSwofraude2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2b) mEgyptABWVSwofraude2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2c) mEgyptABWVSwofraude2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2c2) mEgyptABWVSwofraude2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2d) mEgyptABWVSwofraude2e <- lm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2e) mEgyptABWVSwofraude2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2f) mEgyptABWVSwofraude2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude2f2) table(EgyptABWVSwofraude$PolLead) table(EgyptABWVSwofraude$country) mEgyptABWVSwofraude3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3a) mEgyptABWVSwofraude3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3b) mEgyptABWVSwofraude3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3c) mEgyptABWVSwofraude3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3c2) mEgyptABWVSwofraude3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3d) mEgyptABWVSwofraude3e <- lm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3e) mEgyptABWVSwofraude3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = EgyptABWVSwofraude) summary(mEgyptABWVSwofraude3f) ################################################### ################IRAQ: PERCENT MATCH################ ############################AB##################### ################################################## table(dmAB5$country) Iraq <- dmAB5 %>% mutate(country = as.factor(country)) %>% filter(country == "Iraq") table(Iraq$Surveytype) IraqABmat <- as.matrix(Iraq) percent_match_IraqAB <- c() for(i in seq(nrow(IraqABmat))){ vec <- IraqABmat[i,] dat <- IraqABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_IraqAB[i] <- max(diff) } qplot(percent_match_IraqAB) summary(percent_match_IraqAB) IraqAB3 <- as_tibble(IraqABmat) is.numeric(percent_match_IraqAB) IraqAB3$matchover85IraqAB <- rep(NA,2461) IraqAB3$matchover85IraqAB[percent_match_IraqAB <= 0.849] <-0 IraqAB3$matchover85IraqAB[percent_match_IraqAB >= 0.850] <-1 table(IraqAB3$matchover85IraqAB) # 6 cases prop.table(table(IraqAB3$matchover85IraqAB)) ##########Iraq: PERCENT MATCH################ ###############WVS##################### IraqWVS <- dmWV7 %>% mutate(country = as.factor(B_COUNTRY)) %>% filter(country == "Iraq") table(IraqWVS$Surveytype) IraqWVSmat <- as.matrix(IraqWVS) percent_match_IraqWVS <- c() for(i in seq(nrow(IraqWVSmat))){ vec <- IraqWVSmat[i,] dat <- IraqWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_IraqWVS[i] <- max(diff) } qplot(percent_match_IraqWVS) summary(percent_match_IraqWVS) IraqWVS2 <- as_tibble(IraqWVSmat) IraqWVS2$matchover85IraqWVS <- rep(NA,1200) IraqWVS2$matchover85IraqWVS[percent_match_IraqWVS <= 0.849] <-0 IraqWVS2$matchover85IraqWVS[percent_match_IraqWVS >= 0.850] <-1 table(IraqWVS2$matchover85IraqWVS) # 27. prop.table(table(IraqWVS2$matchover85IraqWVS)) ################################################### ##############Jordan: PERCENT MATCH################ ############################AB##################### ################################################### table(dmAB5$country) Jordan <- dmAB5 %>% mutate(country = as.factor(country)) %>% filter(country == "Jordan") table(Jordan$Surveytype) JordanABmat <- as.matrix(Jordan) percent_match_JordanAB <- c() for(i in seq(nrow(JordanABmat))){ vec <- JordanABmat[i,] dat <- JordanABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_JordanAB[i] <- max(diff) } qplot(percent_match_JordanAB) summary(percent_match_JordanAB) JordanAB3 <- as_tibble(JordanABmat) is.numeric(percent_match_JordanAB) JordanAB3$matchover85JordanAB <- rep(NA,2400) JordanAB3$matchover85JordanAB[percent_match_JordanAB <= 0.849] <-0 JordanAB3$matchover85JordanAB[percent_match_JordanAB >= 0.850] <-1 table(JordanAB3$matchover85JordanAB) # prop.table(table(JordanAB3$matchover85JordanAB)) ##########Jordan: PERCENT MATCH################ ###############WVS##################### JordanWVS <- dmWV7 %>% mutate(country = as.factor(B_COUNTRY)) %>% filter(country == "Jordan") table(Jordan$Surveytype) JordanWVSmat <- as.matrix(JordanWVS) percent_match_JordanWVS <- c() for(i in seq(nrow(JordanWVSmat))){ vec <- JordanWVSmat[i,] dat <- JordanWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_JordanWVS[i] <- max(diff) } qplot(percent_match_JordanWVS) summary(percent_match_JordanWVS) JordanWVS2 <- as_tibble(JordanWVSmat) JordanWVS2$matchover85JordanWVS <- rep(NA,2400) JordanWVS2$matchover85JordanWVS[percent_match_JordanWVS <= 0.849] <-0 JordanWVS2$matchover85JordanWVS[percent_match_JordanWVS >= 0.850] <-1 table(JordanWVS2$matchover85JordanWVS) # prop.table(table(JordanWVS2$matchover85JordanWVS)) ################################################### #############Lebanon: PERCENT MATCH################ ############################AB##################### ################################################### table(dmAB5$country) Lebanon <- dmAB5 %>% mutate(country = as.factor(country)) %>% filter(country == "Lebanon") table(Lebanon$country) LebanonABmat <- as.matrix(Lebanon) percent_match_LebanonAB <- c() for(i in seq(nrow(LebanonABmat))){ vec <- LebanonABmat[i,] dat <- LebanonABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_LebanonAB[i] <- max(diff) } qplot(percent_match_LebanonAB) summary(percent_match_LebanonAB) LebanonAB3 <- as_tibble(LebanonABmat) is.numeric(percent_match_LebanonAB) LebanonAB3$matchover85LebanonAB <- rep(NA,2400) LebanonAB3$matchover85LebanonAB[percent_match_LebanonAB <= 0.849] <-0 LebanonAB3$matchover85LebanonAB[percent_match_LebanonAB >= 0.850] <-1 table(LebanonAB3$matchover85LebanonAB) #90. prop.table(table(LebanonAB3$matchover85LebanonAB)) ##########Lebanon: PERCENT MATCH################ ###############WVS##################### LebanonWVS <- dmWV7 %>% mutate(country = as.factor(B_COUNTRY)) %>% filter(country == "Lebanon") table(LebanonWVS$country) LebanonWVSmat <- as.matrix(LebanonWVS) percent_match_LebanonWVS <- c() for(i in seq(nrow(LebanonWVSmat))){ vec <- LebanonWVSmat[i,] dat <- LebanonWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_LebanonWVS[i] <- max(diff) } qplot(percent_match_LebanonWVS) summary(percent_match_LebanonWVS) LebanonWVS2 <- as_tibble(LebanonWVSmat) LebanonWVS2$matchover85LebanonWVS <- rep(NA,1200) LebanonWVS2$matchover85LebanonWVS[percent_match_LebanonWVS <= 0.849] <-0 LebanonWVS2$matchover85LebanonWVS[percent_match_LebanonWVS >= 0.850] <-1 table(LebanonWVS2$matchover85LebanonWVS) # prop.table(table(LebanonWVS2$matchover85LebanonWVS)) ################################################### #############Tunisia: PERCENT MATCH################ ############################AB##################### ################################################### table(dmAB5$country) Tunisia <- dmAB5 %>% mutate(country = as.factor(country)) %>% filter(country == "Tunisia") table(Tunisia$Surveytype) TunisiaABmat <- as.matrix(Tunisia) percent_match_TunisiaAB <- c() for(i in seq(nrow(TunisiaABmat))){ vec <- TunisiaABmat[i,] dat <- TunisiaABmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_TunisiaAB[i] <- max(diff) } qplot(percent_match_TunisiaAB) summary(percent_match_TunisiaAB) TunisiaAB3 <- as_tibble(TunisiaABmat) is.numeric(percent_match_TunisiaAB) TunisiaAB3$matchover85TunisiaAB <- rep(NA,2400) TunisiaAB3$matchover85TunisiaAB[percent_match_TunisiaAB <= 0.849] <-0 TunisiaAB3$matchover85TunisiaAB[percent_match_TunisiaAB >= 0.850] <-1 table(TunisiaAB3$matchover85TunisiaAB) # prop.table(table(TunisiaAB3$matchover85TunisiaAB)) ##########Tunisia: PERCENT MATCH################ ###############WVS##################### TunisiaWVS <- dmWV7 %>% mutate(country = as.factor(B_COUNTRY)) %>% filter(country == "Tunisia") table(TunisiaWVS$Surveytype) TunisiaWVSmat <- as.matrix(TunisiaWVS) percent_match_TunisiaWVS <- c() for(i in seq(nrow(TunisiaWVSmat))){ vec <- TunisiaWVSmat[i,] dat <- TunisiaWVSmat[-i,] diff <- c() for(j in seq(nrow(dat))){ diff[j] <- (vec == dat[j,]) %>% mean(na.rm = T) } percent_match_TunisiaWVS[i] <- max(diff) } qplot(percent_match_TunisiaWVS) summary(percent_match_TunisiaWVS) TunisiaWVS2 <- as_tibble(TunisiaWVSmat) TunisiaWVS2$matchover85TunisiaWVS <- rep(NA,1204) TunisiaWVS2$matchover85TunisiaWVS[percent_match_TunisiaWVS <= 0.849] <-0 TunisiaWVS2$matchover85TunisiaWVS[percent_match_TunisiaWVS >= 0.850] <-1 table(TunisiaWVS2$matchover85TunisiaWVS) # prop.table(table(TunisiaWVS2$matchover85TunisiaWVS)) ################################################################################################################## ################################################################################################################## ####################################ANALYSIS 7: FLAT REGRESSIONS, PER COUNTRY##################################### ######################################IN EXCEL FILE: "RE-CHECKING OLD ANALYSES"################################### ################################################################################################################## ################################################################################################################## ################################################ ######################EGYPT##################### ################################################ Egypt <- ABWVS %>% mutate(country = as.factor(country)) %>% filter(country == "Egypt") table(ABWVS$country) table(Egypt$country) mEgypt1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Egypt, family = binomial) summary(mEgypt1a) mEgypt1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = Egypt, family = binomial) summary(mEgypt1b) mEgypt1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = Egypt, family = binomial) summary(mEgypt1c) mEgypt1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl , data = Egypt, family = binomial) summary(mEgypt1c2) mEgypt1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt, family = binomial) summary(mEgypt1d) mEgypt1e <- glm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype+ age_short + Educ + singled + othermaritald+ Empl, data = Egypt, family = binomial) summary(mEgypt1e) mEgypt1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt, family = binomial) summary(mEgypt1f) mEgypt1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Egypt, family = binomial) summary(mEgypt1f2) table(Egypt$Unieduc) table(Egypt$country) mEgypt2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt2a) mEgypt2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt2b) mEgypt2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt2c) mEgypt2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt2c2) mEgypt2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt2d) mEgypt2e <- lm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + singled + othermaritald + age_short + Educ + Empl, data = Egypt) summary(mEgypt2e) mEgypt2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt2f) mEgypt2f2 <- lm(Unieduc ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt2f2) table(Egypt$PolLead) table(Egypt$country) mEgypt3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt3a) mEgypt3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt3b) mEgypt3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt3c) mEgypt3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt3c2) mEgypt3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt3d) mEgypt3e <- lm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + singled + othermaritald + age_short + Educ + Empl, data = Egypt) summary(mEgypt3e) mEgypt3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt3f) table(Egypt$Trustpol) table(Egypt$country) table(Egypt$Trustpol, Egypt$Surveytype) mEgypt4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt4a) mEgypt4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt4b) mEgypt4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt4c) mEgypt4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt4d) mEgypt4d2 <- lm(Trustpol ~ surveytype2 + gender + Educ*surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt4d2) mEgypt4e <- lm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt4e) mEgypt4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt4f) mEgypt4f2 <- lm(Trustpol ~ surveytype2 + gender + Empl*surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Egypt) summary(mEgypt4f2) ################################################ #######################IRAQ##################### ################################################ Iraq <- ABWVS %>% mutate(country = as.factor(country)) %>% filter(country == "Iraq") table(ABWVS$country) table(Iraq$country) mIraq1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Iraq, family = binomial) summary(mIraq1a) mIraq1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = Iraq, family = binomial) summary(mIraq1b) mIraq1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = Iraq, family = binomial) summary(mIraq1c) mIraq1c2 <- glm(Trust ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl , data = Iraq, family = binomial) summary(mIraq1c2) mIraq1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq, family = binomial) summary(mIraq1d) mIraq1e <- glm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq, family = binomial) summary(mIraq1e) mIraq1e2 <- glm(Trust ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq, family = binomial) summary(mIraq1e2) mIraq1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq, family = binomial) summary(mIraq1f) table(Iraq$Unieduc) table(Iraq$country) mIraq2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq2a) mIraq2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq2b) mIraq2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq2c) mIraq2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq2d) mIraq2d2 <- lm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq2d2) mIraq2e <- lm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq2e) mIraq2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq2f) table(Iraq$PolLead) table(Iraq$country) mIraq3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq3a) mIraq3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq3b) mIraq3b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq3b2) mIraq3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq3c) mIraq3c2 <- lm(PolLead ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq3c2) mIraq3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq3d) mIraq3e <- lm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq3e) mIraq3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq3f) table(Iraq$Trustpol) table(Iraq$country) mIraq4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq4a) mIraq4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq4b) mIraq4b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq4b2) mIraq4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq4c) mIraq4c2 <- lm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq4c2) mIraq4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq4d) mIraq4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq4d2) mIraq4e <- lm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq4e) mIraq4e2 <- lm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq4e2) mIraq4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Iraq) summary(mIraq4f) ################################################ #####################JORDAN##################### ################################################ Jordan <- ABWVS %>% mutate(country = as.factor(country)) %>% filter(country == "Jordan") table(ABWVS$country) table(Jordan$country) mJordan1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Jordan, family = binomial) summary(mJordan1a) mJordan1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = Jordan, family = binomial) summary(mJordan1b) mJordan1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = Jordan, family = binomial) summary(mJordan1c) mJordan1d2 <- glm(Trust ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Jordan, family = binomial) summary(mJordan1d2) mJordan1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan, family = binomial) summary(mJordan1d) mJordan1e <- glm(Trust ~ Surveytype + gender + + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan, family = binomial) summary(mJordan1e) mJordan1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan, family = binomial) summary(mJordan1f) table(Jordan$Unieduc) table(Jordan$country) mJordan2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan2a) mJordan2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan2b) mJordan2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan2c) mJordan2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan2d) mJordan2e <- lm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan2e) mJordan2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan2f) table(Jordan$PolLead) table(Jordan$country) mJordan3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan3a) mJordan3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan3b) mJordan3b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan3b2) mJordan3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan3c) mJordan3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan3d) mJordan3e <- lm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan3e) mJordan3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan3f) table(Jordan$Trustpol) table(Jordan$country) mJordan4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan4a) mJordan4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan4b) mJordan4b2 <- lm(Trustpol ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan4b2) mJordan4c2 <- lm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan4c2) mJordan4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan4c) mJordan4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan4d) mJordan4e <- lm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan4e) mJordan4e2 <- lm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan4e2) mJordan4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Jordan) summary(mJordan4f) ################################################ ####################LEBANON##################### ################################################ Lebanon <- ABWVS %>% mutate(country = as.factor(country)) %>% filter(country == "Lebanon") table(ABWVS$country) table(Lebanon$country) mLebanon1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Lebanon, family = binomial) summary(mLebanon1a) mLebanon1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = Lebanon, family = binomial) summary(mLebanon1b) mLebanon1b2 <- glm(Trust ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl , data = Lebanon, family = binomial) summary(mLebanon1b2) mLebanon1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = Lebanon, family = binomial) summary(mLebanon1c) mLebanon1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon, family = binomial) summary(mLebanon1d) mLebanon1e <- glm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon, family = binomial) summary(mLebanon1e) mLebanon1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon, family = binomial) summary(mLebanon1f) mLebanon1f2 <- glm(Trust ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon, family = binomial) summary(mLebanon1f2) table(Lebanon$Unieduc) table(Lebanon$country) mLebanon2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon2a) mLebanon2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon2b) mLebanon2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon2c) mLebanon2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon2d) mLebanon2d2 <- lm(Unieduc ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon2d2) mLebanon2e <- lm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon2e) mLebanon2e2 <- lm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon2e2) mLebanon2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon2f) table(Lebanon$PolLead) table(Lebanon$country) mLebanon3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon3a) mLebanon3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon3b) mLebanon3b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon3b2) mLebanon3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon3c) mLebanon3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon3d) mLebanon3d2 <- lm(PolLead ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon3d2) mLebanon3e <- lm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon3e) mLebanon3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon3f) table(Lebanon$Trustpol) table(Lebanon$country) mLebanon4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon4a) mLebanon4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon4b) mLebanon4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon4c) mLebanon4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon4d) mLebanon4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon4d2) mLebanon4e <- lm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon4e) mLebanon4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon4f) mLebanon4f2 <- lm(Trustpol ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Lebanon) summary(mLebanon4f2) ################################################ ####################TUNISIA##################### ################################################ Tunisia <- ABWVS %>% mutate(country = as.factor(country)) %>% filter(country == "Tunisia") table(ABWVS$country) table(Tunisia$country) mTunisia1a <- glm(Trust ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Tunisia, family = binomial) summary(mTunisia1a) mTunisia1b <- glm(Trust ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = Tunisia, family = binomial) summary(mTunisia1b) mTunisia1c <- glm(Trust ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl , data = Tunisia, family = binomial) summary(mTunisia1c) mTunisia1d <- glm(Trust ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia, family = binomial) summary(mTunisia1d) mTunisia1e <- glm(Trust ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia, family = binomial) summary(mTunisia1e) mTunisia1f <- glm(Trust ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia, family = binomial) summary(mTunisia1f) table(Tunisia$Unieduc) table(Tunisia$country) mTunisia2a <- lm(Unieduc ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia2a) mTunisia2b <- lm(Unieduc ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia2b) mTunisia2b2 <- lm(Unieduc ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia2b2) mTunisia2c <- lm(Unieduc ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia2c) mTunisia2c2 <- lm(Unieduc ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia2c2) mTunisia2d <- lm(Unieduc ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia2d) mTunisia2e <- lm(Unieduc ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia2e) mTunisia2e2 <- lm(Unieduc ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia2e2) mTunisia2f <- lm(Unieduc ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia2f) table(Tunisia$PolLead) table(Tunisia$country) mTunisia3a <- lm(PolLead ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia3a) mTunisia3b <- lm(PolLead ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia3b) mTunisia3b2 <- lm(PolLead ~ Surveytype2 + gender + gender*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia3b2) mTunisia3c <- lm(PolLead ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia3c) mTunisia3d <- lm(PolLead ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia3d) mTunisia3e <- lm(PolLead ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia3e) mTunisia3e2 <- lm(PolLead ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia3e2) mTunisia3f <- lm(PolLead ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia3f) mTunisia3f2 <- lm(PolLead ~ Surveytype2 + gender + Empl*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia3f2) table(Tunisia$Trustpol) table(Tunisia$country) mTunisia4a <- lm(Trustpol ~ Surveytype + gender + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia4a) mTunisia4b <- lm(Trustpol ~ Surveytype + gender + gender*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia4b) mTunisia4c <- lm(Trustpol ~ Surveytype + gender + age_short*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia4c) mTunisia4c2 <- lm(Trustpol ~ Surveytype2 + gender + age_short*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia4c2) mTunisia4d <- lm(Trustpol ~ Surveytype + gender + Educ*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia4d) mTunisia4d2 <- lm(Trustpol ~ Surveytype2 + gender + Educ*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia4d2) mTunisia4e <- lm(Trustpol ~ Surveytype + gender + singled*Surveytype + othermaritald*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia4e) mTunisia4e2 <- lm(Trustpol ~ Surveytype2 + gender + singled*Surveytype2 + othermaritald*Surveytype2 + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia4e2) mTunisia4f <- lm(Trustpol ~ Surveytype + gender + Empl*Surveytype + age_short + Educ + singled + othermaritald + Empl, data = Tunisia) summary(mTunisia4f)