
R version 4.0.5 (2021-03-31) -- "Shake and Throw"
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> library(metafor)
Lade nötiges Paket: Matrix
Lade nötiges Paket: metadat
Lade nötiges Paket: numDeriv

Loading the 'metafor' package (version 4.0-0). For an
introduction to the package please type: help(metafor)

> dat <- read.table("mode27.txt", header=T, na.strings = "-99")
> attach(dat)
> dat <- escalc(measure="PFT", xi=xi, ni=ni, data=dat, slab=paste(Authors, pubyear, sep=", "))
> res <- rma(yi, vi, method="HS", data=dat)
> res

Random-Effects Model (k = 64; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0304 (SE = 0.0126)
tau (square root of estimated tau^2 value):      0.1745
I^2 (total heterogeneity / total variability):   99.86%
H^2 (total variability / sampling variability):  703.23

Test for Heterogeneity:
Q(df = 63) = 60922.7440, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5496  0.0220  25.0318  <.0001  0.5065  0.5926  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=dat$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2724 0.2349 0.3116 0.0406 0.6082 

> res <- rma(yi, vi, method="HS", mods = ~ expert, data=dat)
> res

Mixed-Effects Model (k = 64; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0304 (SE = 0.0123)
tau (square root of estimated tau^2 value):             0.1745
I^2 (residual heterogeneity / unaccounted variability): 99.86%
H^2 (unaccounted variability / sampling variability):   699.15
R^2 (amount of heterogeneity accounted for):            0.00%

Test for Residual Heterogeneity:
QE(df = 62) = 60920.4907, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 1.6302, p-val = 0.2017

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt    0.5336  0.0253  21.1168  <.0001   0.4841  0.5831  *** 
expert     0.0652  0.0510   1.2768  0.2017  -0.0349  0.1652      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> 
> 
> res <- rma(yi, vi, method="HS", mods = ~ samplegp, data=dat)
> res

Mixed-Effects Model (k = 64; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0186 (SE = 0.0038)
tau (square root of estimated tau^2 value):             0.1365
I^2 (residual heterogeneity / unaccounted variability): 99.69%
H^2 (unaccounted variability / sampling variability):   323.06
R^2 (amount of heterogeneity accounted for):            38.80%

Test for Residual Heterogeneity:
QE(df = 62) = 37307.7036, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 4.7161, p-val = 0.0299

Model Results:

          estimate      se     zval    pval    ci.lb    ci.ub      
intrcpt     0.5857  0.0241  24.2598  <.0001   0.5384   0.6331  *** 
samplegp   -0.0749  0.0345  -2.1717  0.0299  -0.1425  -0.0073    * 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ countryAm, data=dat)
> res

Mixed-Effects Model (k = 64; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0299 (SE = 0.0090)
tau (square root of estimated tau^2 value):             0.1729
I^2 (residual heterogeneity / unaccounted variability): 99.83%
H^2 (unaccounted variability / sampling variability):   584.49
R^2 (amount of heterogeneity accounted for):            1.83%

Test for Residual Heterogeneity:
QE(df = 62) = 59806.5997, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 1.0073, p-val = 0.3156

Model Results:

           estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt      0.5659  0.0272  20.8313  <.0001   0.5126  0.6191  *** 
countryAm   -0.0455  0.0454  -1.0036  0.3156  -0.1344  0.0434      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ health, data=dat)
> res

Mixed-Effects Model (k = 64; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0295 (SE = 0.0088)
tau (square root of estimated tau^2 value):             0.1716
I^2 (residual heterogeneity / unaccounted variability): 99.83%
H^2 (unaccounted variability / sampling variability):   575.27
R^2 (amount of heterogeneity accounted for):            3.22%

Test for Residual Heterogeneity:
QE(df = 62) = 58962.7337, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.8481, p-val = 0.3571

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt    0.5760  0.0360  16.0162  <.0001   0.5055  0.6465  *** 
health    -0.0414  0.0450  -0.9209  0.3571  -0.1296  0.0467      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ method, data=dat)
> res

Mixed-Effects Model (k = 64; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0277 (SE = 0.0073)
tau (square root of estimated tau^2 value):             0.1664
I^2 (residual heterogeneity / unaccounted variability): 99.80%
H^2 (unaccounted variability / sampling variability):   505.60
R^2 (amount of heterogeneity accounted for):            9.09%

Test for Residual Heterogeneity:
QE(df = 62) = 55388.7355, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.7897, p-val = 0.3742

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt    0.5400  0.0235  22.9952  <.0001   0.4940  0.5861  *** 
method     0.0461  0.0519   0.8886  0.3742  -0.0556  0.1479      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ fieldp57more, data=dat)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 56; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0277 (SE = 0.0102)
tau (square root of estimated tau^2 value):             0.1665
I^2 (residual heterogeneity / unaccounted variability): 99.83%
H^2 (unaccounted variability / sampling variability):   600.78
R^2 (amount of heterogeneity accounted for):            1.63%

Test for Residual Heterogeneity:
QE(df = 54) = 51589.7411, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.0058, p-val = 0.9390

Model Results:

              estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt         0.5341  0.0292  18.2893  <.0001   0.4769  0.5914  *** 
fieldp57more    0.0035  0.0456   0.0765  0.9390  -0.0858  0.0928      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ intRem014, data=dat)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 51; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0249 (SE = 0.0086)
tau (square root of estimated tau^2 value):             0.1579
I^2 (residual heterogeneity / unaccounted variability): 99.82%
H^2 (unaccounted variability / sampling variability):   549.39
R^2 (amount of heterogeneity accounted for):            10.87%

Test for Residual Heterogeneity:
QE(df = 49) = 45522.5314, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.1621, p-val = 0.6872

Model Results:

           estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt      0.5122  0.0376  13.6266  <.0001   0.4385  0.5859  *** 
intRem014   -0.0188  0.0467  -0.4027  0.6872  -0.1103  0.0727      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ lenghtmedmore, data=dat)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 38; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0088 (SE = 0.0022)
tau (square root of estimated tau^2 value):             0.0935
I^2 (residual heterogeneity / unaccounted variability): 99.37%
H^2 (unaccounted variability / sampling variability):   158.30
R^2 (amount of heterogeneity accounted for):            48.57%

Test for Residual Heterogeneity:
QE(df = 36) = 13295.4692, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.0199, p-val = 0.8877

Model Results:

               estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt          0.5458  0.0226  24.1392  <.0001   0.5015  0.5901  *** 
lenghtmedmore    0.0044  0.0309   0.1412  0.8877  -0.0563  0.0650      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ reminder3more, data=dat)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 61; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0380 (SE = 0.0128)
tau (square root of estimated tau^2 value):             0.1948
I^2 (residual heterogeneity / unaccounted variability): 99.82%
H^2 (unaccounted variability / sampling variability):   560.06
R^2 (amount of heterogeneity accounted for):            1.26%

Test for Residual Heterogeneity:
QE(df = 59) = 36370.5560, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.2812, p-val = 0.5959

Model Results:

               estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt          0.5586  0.0318  17.5545  <.0001   0.4962  0.6210  *** 
reminder3more   -0.0274  0.0517  -0.5303  0.5959  -0.1288  0.0739      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ personalized, data=dat)
> res

Mixed-Effects Model (k = 64; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0302 (SE = 0.0111)
tau (square root of estimated tau^2 value):             0.1737
I^2 (residual heterogeneity / unaccounted variability): 99.85%
H^2 (unaccounted variability / sampling variability):   652.50
R^2 (amount of heterogeneity accounted for):            0.85%

Test for Residual Heterogeneity:
QE(df = 62) = 60407.8393, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.0005, p-val = 0.9823

Model Results:

              estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt         0.5499  0.0261  21.0685  <.0001   0.4987  0.6010  *** 
personalized   -0.0011  0.0478  -0.0222  0.9823  -0.0947  0.0926      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ incentive, data=dat)
> res

Mixed-Effects Model (k = 64; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0302 (SE = 0.0110)
tau (square root of estimated tau^2 value):             0.1739
I^2 (residual heterogeneity / unaccounted variability): 99.85%
H^2 (unaccounted variability / sampling variability):   649.99
R^2 (amount of heterogeneity accounted for):            0.66%

Test for Residual Heterogeneity:
QE(df = 62) = 60521.2836, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.0027, p-val = 0.9588

Model Results:

           estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt      0.5487  0.0277  19.8303  <.0001   0.4945  0.6029  *** 
incentive    0.0023  0.0452   0.0516  0.9588  -0.0863  0.0910      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ rr51100, data=dat)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 61; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0272 (SE = 0.0094)
tau (square root of estimated tau^2 value):             0.1649
I^2 (residual heterogeneity / unaccounted variability): 99.83%
H^2 (unaccounted variability / sampling variability):   580.58
R^2 (amount of heterogeneity accounted for):            11.32%

Test for Residual Heterogeneity:
QE(df = 59) = 53707.7618, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.2576, p-val = 0.6118

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt    0.5335  0.0309  17.2679  <.0001   0.4729  0.5940  *** 
rr51100    0.0216  0.0426   0.5076  0.6118  -0.0619  0.1051      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


___________________________________________________________________________________
___________________________________________________________________________________

> data1 <- subset(dat, mode == 1)
> res <- rma(yi, vi, method="HS", mods = ~ expert, data=data1)
> res

Mixed-Effects Model (k = 17; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0115 (SE = 0.0032)
tau (square root of estimated tau^2 value):             0.1074
I^2 (residual heterogeneity / unaccounted variability): 99.70%
H^2 (unaccounted variability / sampling variability):   336.07
R^2 (amount of heterogeneity accounted for):            0.02%

Test for Residual Heterogeneity:
QE(df = 15) = 14800.4863, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 2.2973, p-val = 0.1296

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt    0.5434  0.0299  18.1589  <.0001   0.4847  0.6021  *** 
expert     0.0970  0.0640   1.5157  0.1296  -0.0284  0.2224      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ samplegp, data=data1)
> res

Mixed-Effects Model (k = 17; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0034 (SE = 0.0005)
tau (square root of estimated tau^2 value):             0.0584
I^2 (residual heterogeneity / unaccounted variability): 98.56%
H^2 (unaccounted variability / sampling variability):   69.31
R^2 (amount of heterogeneity accounted for):            70.47%

Test for Residual Heterogeneity:
QE(df = 15) = 4382.9543, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 14.0046, p-val = 0.0002

Model Results:

          estimate      se     zval    pval    ci.lb    ci.ub      
intrcpt     0.5934  0.0171  34.7341  <.0001   0.5599   0.6269  *** 
samplegp   -0.1271  0.0340  -3.7423  0.0002  -0.1937  -0.0606  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ rr51100, data=data1)
> res

Mixed-Effects Model (k = 17; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0053 (SE = 0.0009)
tau (square root of estimated tau^2 value):             0.0726
I^2 (residual heterogeneity / unaccounted variability): 99.11%
H^2 (unaccounted variability / sampling variability):   112.93
R^2 (amount of heterogeneity accounted for):            54.26%

Test for Residual Heterogeneity:
QE(df = 15) = 6780.2712, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 3.4993, p-val = 0.0614

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt    0.5317  0.0246  21.6108  <.0001   0.4835  0.5799  *** 
rr51100    0.0682  0.0365   1.8706  0.0614  -0.0033  0.1396    . 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ countryAm, data=data1)
> res

Mixed-Effects Model (k = 17; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0093 (SE = 0.0022)
tau (square root of estimated tau^2 value):             0.0965
I^2 (residual heterogeneity / unaccounted variability): 99.58%
H^2 (unaccounted variability / sampling variability):   239.47
R^2 (amount of heterogeneity accounted for):            19.29%

Test for Residual Heterogeneity:
QE(df = 15) = 11951.7368, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 11.1930, p-val = 0.0008

Model Results:

           estimate      se     zval    pval   ci.lb   ci.ub      
intrcpt      0.4584  0.0396  11.5773  <.0001  0.3808  0.5360  *** 
countryAm    0.1659  0.0496   3.3456  0.0008  0.0687  0.2631  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ health, data=data1)
> res

Mixed-Effects Model (k = 17; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0107 (SE = 0.0026)
tau (square root of estimated tau^2 value):             0.1032
I^2 (residual heterogeneity / unaccounted variability): 99.64%
H^2 (unaccounted variability / sampling variability):   276.82
R^2 (amount of heterogeneity accounted for):            7.69%

Test for Residual Heterogeneity:
QE(df = 15) = 13666.1880, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 4.8389, p-val = 0.0278

Model Results:

         estimate      se     zval    pval    ci.lb    ci.ub      
intrcpt    0.6106  0.0330  18.5183  <.0001   0.5460   0.6752  *** 
health    -0.1140  0.0518  -2.1998  0.0278  -0.2157  -0.0124    * 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ method, data=data1)
> res

Mixed-Effects Model (k = 17; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0040 (SE = 0.0006)
tau (square root of estimated tau^2 value):             0.0636
I^2 (residual heterogeneity / unaccounted variability): 98.73%
H^2 (unaccounted variability / sampling variability):   78.72
R^2 (amount of heterogeneity accounted for):            64.97%

Test for Residual Heterogeneity:
QE(df = 15) = 5197.5611, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 24.3043, p-val < .0001

Model Results:

         estimate      se     zval    pval   ci.lb   ci.ub      
intrcpt    0.4931  0.0212  23.2189  <.0001  0.4514  0.5347  *** 
method     0.1592  0.0323   4.9299  <.0001  0.0959  0.2226  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ fieldp57more, data=data1)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 15; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0113 (SE = 0.0031)
tau (square root of estimated tau^2 value):             0.1061
I^2 (residual heterogeneity / unaccounted variability): 99.72%
H^2 (unaccounted variability / sampling variability):   352.91
R^2 (amount of heterogeneity accounted for):            1.56%

Test for Residual Heterogeneity:
QE(df = 13) = 14120.4567, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 9.4509, p-val = 0.0021

Model Results:

              estimate      se     zval    pval   ci.lb   ci.ub      
intrcpt         0.5111  0.0339  15.0968  <.0001  0.4448  0.5775  *** 
fieldp57more    0.1835  0.0597   3.0742  0.0021  0.0665  0.3005   ** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ lenghtmedmore, data=data1)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 13; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0037 (SE = 0.0005)
tau (square root of estimated tau^2 value):             0.0610
I^2 (residual heterogeneity / unaccounted variability): 98.69%
H^2 (unaccounted variability / sampling variability):   76.38
R^2 (amount of heterogeneity accounted for):            63.45%

Test for Residual Heterogeneity:
QE(df = 11) = 4582.9720, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 1.9566, p-val = 0.1619

Model Results:

               estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt          0.6203  0.0223  27.8668  <.0001   0.5767  0.6640  *** 
lenghtmedmore   -0.0500  0.0357  -1.3988  0.1619  -0.1200  0.0201      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ reminder3more, data=data1)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 14; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0165 (SE = 0.0080)
tau (square root of estimated tau^2 value):             0.1285
I^2 (residual heterogeneity / unaccounted variability): 99.58%
H^2 (unaccounted variability / sampling variability):   237.49
R^2 (amount of heterogeneity accounted for):            9.16%

Test for Residual Heterogeneity:
QE(df = 12) = 3958.1793, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.2924, p-val = 0.5887

Model Results:

               estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt          0.5453  0.0463  11.7740  <.0001   0.4545  0.6361  *** 
reminder3more    0.0379  0.0702   0.5407  0.5887  -0.0996  0.1755      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ personalized, data=data1)
> res

Mixed-Effects Model (k = 17; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0110 (SE = 0.0028)
tau (square root of estimated tau^2 value):             0.1047
I^2 (residual heterogeneity / unaccounted variability): 99.67%
H^2 (unaccounted variability / sampling variability):   303.08
R^2 (amount of heterogeneity accounted for):            5.00%

Test for Residual Heterogeneity:
QE(df = 15) = 14064.1008, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.2339, p-val = 0.6286

Model Results:

              estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt         0.5525  0.0358  15.4457  <.0001   0.4824  0.6226  *** 
personalized    0.0250  0.0516   0.4837  0.6286  -0.0762  0.1262      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ incentive, data=data1)
> res

Mixed-Effects Model (k = 17; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0107 (SE = 0.0025)
tau (square root of estimated tau^2 value):             0.1037
I^2 (residual heterogeneity / unaccounted variability): 99.63%
H^2 (unaccounted variability / sampling variability):   271.10
R^2 (amount of heterogeneity accounted for):            6.86%

Test for Residual Heterogeneity:
QE(df = 15) = 13789.4397, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 1.0170, p-val = 0.3132

Model Results:

           estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt      0.5337  0.0398  13.4047  <.0001   0.4557  0.6117  *** 
incentive    0.0524  0.0519   1.0085  0.3132  -0.0494  0.1541      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1



___________________________________________________________________________________
___________________________________________________________________________________


> data2 <- subset(dat, mode == 2)
> res <- rma(yi, vi, method="HS", mods = ~ expert, data=data2)
> res

Mixed-Effects Model (k = 47; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0449 (SE = 0.0175)
tau (square root of estimated tau^2 value):             0.2118
I^2 (residual heterogeneity / unaccounted variability): 99.84%
H^2 (unaccounted variability / sampling variability):   630.23
R^2 (amount of heterogeneity accounted for):            0.69%

Test for Residual Heterogeneity:
QE(df = 45) = 32241.2042, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.6088, p-val = 0.4352

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt    0.5299  0.0359  14.7616  <.0001   0.4596  0.6003  *** 
expert     0.0557  0.0714   0.7803  0.4352  -0.0843  0.1958      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ samplegp, data=data2)
> res

Mixed-Effects Model (k = 47; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0439 (SE = 0.0169)
tau (square root of estimated tau^2 value):             0.2094
I^2 (residual heterogeneity / unaccounted variability): 99.84%
H^2 (unaccounted variability / sampling variability):   611.24
R^2 (amount of heterogeneity accounted for):            2.90%

Test for Residual Heterogeneity:
QE(df = 45) = 31523.7763, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 1.0052, p-val = 0.3161

Model Results:

          estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt     0.5800  0.0472  12.2853  <.0001   0.4874  0.6725  *** 
samplegp   -0.0623  0.0621  -1.0026  0.3161  -0.1841  0.0595      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ rr51100, data=data2)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 44; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0452 (SE = 0.0167)
tau (square root of estimated tau^2 value):             0.2127
I^2 (residual heterogeneity / unaccounted variability): 99.83%
H^2 (unaccounted variability / sampling variability):   605.88
R^2 (amount of heterogeneity accounted for):            0.46%

Test for Residual Heterogeneity:
QE(df = 42) = 31302.1706, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.0013, p-val = 0.9711

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt    0.5360  0.0479  11.1915  <.0001   0.4421  0.6298  *** 
rr51100    0.0023  0.0647   0.0362  0.9711  -0.1245  0.1292      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ countryAm, data=data2)
> res

Mixed-Effects Model (k = 47; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0310 (SE = 0.0092)
tau (square root of estimated tau^2 value):             0.1760
I^2 (residual heterogeneity / unaccounted variability): 99.74%
H^2 (unaccounted variability / sampling variability):   379.88
R^2 (amount of heterogeneity accounted for):            31.46%

Test for Residual Heterogeneity:
QE(df = 45) = 22266.0968, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 7.2856, p-val = 0.0070

Model Results:

           estimate      se     zval    pval    ci.lb    ci.ub      
intrcpt      0.5846  0.0299  19.5224  <.0001   0.5259   0.6433  *** 
countryAm   -0.1599  0.0592  -2.6992  0.0070  -0.2761  -0.0438   ** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ health, data=data2)
> res

Mixed-Effects Model (k = 47; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0376 (SE = 0.0111)
tau (square root of estimated tau^2 value):             0.1940
I^2 (residual heterogeneity / unaccounted variability): 99.78%
H^2 (unaccounted variability / sampling variability):   458.64
R^2 (amount of heterogeneity accounted for):            16.66%

Test for Residual Heterogeneity:
QE(df = 45) = 27065.2442, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.0078, p-val = 0.9295

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt    0.5479  0.0541  10.1371  <.0001   0.4420  0.6539  *** 
health    -0.0056  0.0636  -0.0884  0.9295  -0.1302  0.1190      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ method, data=data2)
> res

Mixed-Effects Model (k = 47; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0327 (SE = 0.0092)
tau (square root of estimated tau^2 value):             0.1808
I^2 (residual heterogeneity / unaccounted variability): 99.74%
H^2 (unaccounted variability / sampling variability):   381.64
R^2 (amount of heterogeneity accounted for):            27.61%

Test for Residual Heterogeneity:
QE(df = 45) = 23515.0468, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.2863, p-val = 0.5926

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt    0.5492  0.0284  19.3206  <.0001   0.4935  0.6049  *** 
method    -0.0425  0.0795  -0.5351  0.5926  -0.1983  0.1132      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ fieldp57more, data=data2)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 41; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0238 (SE = 0.0082)
tau (square root of estimated tau^2 value):             0.1544
I^2 (residual heterogeneity / unaccounted variability): 99.66%
H^2 (unaccounted variability / sampling variability):   293.12
R^2 (amount of heterogeneity accounted for):            9.80%

Test for Residual Heterogeneity:
QE(df = 39) = 14482.7725, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.9316, p-val = 0.3344

Model Results:

              estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt         0.5432  0.0325  16.7018  <.0001   0.4795  0.6069  *** 
fieldp57more   -0.0472  0.0489  -0.9652  0.3344  -0.1432  0.0487      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ intRem014, data=data2)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 39; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0173 (SE = 0.0054)
tau (square root of estimated tau^2 value):             0.1314
I^2 (residual heterogeneity / unaccounted variability): 99.51%
H^2 (unaccounted variability / sampling variability):   206.18
R^2 (amount of heterogeneity accounted for):            10.62%

Test for Residual Heterogeneity:
QE(df = 37) = 10182.3184, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.5540, p-val = 0.4567

Model Results:

           estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt      0.5115  0.0314  16.2873  <.0001   0.4499  0.5730  *** 
intRem014   -0.0318  0.0427  -0.7443  0.4567  -0.1154  0.0519      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ lenghtmedmore, data=data2)
Warnmeldung:
Studies with NAs omitted from model fitting. 
> res

Mixed-Effects Model (k = 25; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0157 (SE = 0.0050)
tau (square root of estimated tau^2 value):             0.1252
I^2 (residual heterogeneity / unaccounted variability): 99.21%
H^2 (unaccounted variability / sampling variability):   127.34
R^2 (amount of heterogeneity accounted for):            26.11%

Test for Residual Heterogeneity:
QE(df = 23) = 4488.0131, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 1.4925, p-val = 0.2218

Model Results:

               estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt          0.4804  0.0403  11.9081  <.0001   0.4013  0.5594  *** 
lenghtmedmore    0.0633  0.0518   1.2217  0.2218  -0.0382  0.1648      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ reminder3more, data=data2)
> res

Mixed-Effects Model (k = 47; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0449 (SE = 0.0173)
tau (square root of estimated tau^2 value):             0.2120
I^2 (residual heterogeneity / unaccounted variability): 99.84%
H^2 (unaccounted variability / sampling variability):   623.24
R^2 (amount of heterogeneity accounted for):            0.54%

Test for Residual Heterogeneity:
QE(df = 45) = 32288.8010, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.5783, p-val = 0.4470

Model Results:

               estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt          0.5618  0.0389  14.4411  <.0001   0.4856  0.6381  *** 
reminder3more   -0.0491  0.0646  -0.7605  0.4470  -0.1758  0.0775      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ personalized, data=data2)
> res

Mixed-Effects Model (k = 47; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0338 (SE = 0.0118)
tau (square root of estimated tau^2 value):             0.1840
I^2 (residual heterogeneity / unaccounted variability): 99.77%
H^2 (unaccounted variability / sampling variability):   439.38
R^2 (amount of heterogeneity accounted for):            25.09%

Test for Residual Heterogeneity:
QE(df = 45) = 24331.0737, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.1103, p-val = 0.7398

Model Results:

              estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt         0.5488  0.0309  17.7842  <.0001   0.4883  0.6092  *** 
personalized   -0.0212  0.0637  -0.3322  0.7398  -0.1461  0.1037      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> res <- rma(yi, vi, method="HS", mods = ~ incentive, data=data2)
> res

Mixed-Effects Model (k = 47; tau^2 estimator: HS)

tau^2 (estimated amount of residual heterogeneity):     0.0343 (SE = 0.0120)
tau (square root of estimated tau^2 value):             0.1851
I^2 (residual heterogeneity / unaccounted variability): 99.78%
H^2 (unaccounted variability / sampling variability):   446.02
R^2 (amount of heterogeneity accounted for):            24.16%

Test for Residual Heterogeneity:
QE(df = 45) = 24634.0866, p-val < .0001

Test of Moderators (coefficient 2):
QM(df = 1) = 0.1863, p-val = 0.6660

Model Results:

           estimate      se     zval    pval    ci.lb   ci.ub      
intrcpt      0.5514  0.0324  17.0181  <.0001   0.4879  0.6149  *** 
incentive   -0.0257  0.0594  -0.4316  0.6660  -0.1421  0.0908      

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> 