Survey Research Methods <p><br>Survey Research Methods is the official peer-reviewed journal of the European Survey Research Association (ESRA). The journal publishes articles in English, which discuss methodological issues related to survey research.</p> European Survey Research Association en-US Survey Research Methods 1864-3361 Copyright for articles published in this journal is retained by the authors, with first publication rights granted to the journal. By virtue of their appearance in this open access journal, users can use, reuse and build upon the material published in the journal but only for non-commercial purposes and with proper attribution. Willingness to use mobile technologies for data collection in a probability household panel We asked members of the Understanding Society Innovation Panel about their willingness to participate in various data collection tasks on their mobile devices. We find that stated willingness varies considerably depending on the type of activity involved: respondents are less willing to participate in tasks that involve downloading and installing an app, or where data are collected passively. Stated willingness also varies between smartphones and tablets, and between types of respondents: respondents who report higher concerns about the security of data collected with mobile technologies and those who use their devices less intensively are less willing to participate in mobile data collection tasks. Alexander Wenz Annette Jäckle Mick P. Couper Copyright (c) 2019 Alexander Wenz, Annette Jäckle, Mick P. Couper 2019-04-11 2019-04-11 13 1 1 22 10.18148/srm/2019.v1i1.7298 Participation in a mobile app survey to collect expenditure data as part of a large-scale probability household panel: coverage and participation rates and biases This paper examines non-response in a mobile app study designed to collect expenditure data. We invited 2,383 members of the nationally representative Understanding Society Innovation Panel in Great Britain to download an app to record their spending on goods and services: participants were asked to scan receipts or report spending directly in the app every day for a month. We examine participation at different stages of the process. We further use data from the prior wave of the panel to examine the prevalence of potential barriers to participation, including access, ability and willingness to use different mobile technologies, and biases in the types of people who participate, considering socio-demographic characteristics, financial position and financial behaviours. While the participation rate was low, drop out was also low: over 80% of participants remained in the study for the full month. The main barriers to participation were access to, and frequency of use of mobile devices, willingness to download an app for a survey, and general cooperativeness with the survey. While there were strong biases in who participated in terms of socio-demographic characteristics (with women, younger, and more educated sample members being more likely to participate), and in terms of financial behaviours (with respondents who already use mobile devices to monitor their finances being more likely to participate), we found no biases in correlates of spending. Annette Jäckle Jonathan Burton Mick P. Couper Carli Lessof Copyright (c) 2019 Annette Jäckle, Jonathan Burton, Mick P. Couper, Carli Lessof 2019-04-11 2019-04-11 13 1 23 44 10.18148/srm/2019.v1i1.7297 Respondent burden in a Mobile App: evidence from a shopping receipt scanning study. This study considers the burden placed on participants, subjectively and objectively, when asked to use a mobile app to scan shopping receipts. Using data from both the Understanding Society Spending Study, and the ninth wave of the Understanding Society Innovation Panel allow measures of burden and related characteristics to be identified. Subjective and objective burden were found to be seemingly unrelated to one another. There is evidence of older respondents facing greater objective burden, however there was some evidence that this did not correspond to an increase in the levels of subjective burden reported. Reported willingness to participate in a task of a similar nature proved to be indicative of both objective and subjective burden. Brendan Read Copyright (c) 2019 Brendan Read 2019-04-11 2019-04-11 13 1 45 71 10.18148/srm/2019.v1i1.7379 Tree-based Machine Learning Methods for Survey Research Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys. Christoph Kern Thomas Klausch Frauke Kreuter Copyright (c) 2019 Christoph Kern, Thomas Klausch, Frauke Kreuter 2019-04-11 2019-04-11 13 1 73 93 10.18148/srm/2019.v1i1.7395 A Partially Successful Attempt to Integrate a Web-Recruited Cohort into an Address-Based Sample We use a web-and-mail survey on attitudes towards and use of marijuana to demonstrate how a web-recruited cohort could be integrated into an address-based sample using a calibration-weighting procedure in the software language SUDAAN 11®. A Holm-Bonferroni procedure is employed to test whether a pivotal assumption underlying the integration is supported by the data for individual survey items as well as for the survey as a whole. Delete-a-group jackknife weights for the integrated sample are then developed. Phillip S Kott Copyright (c) 2019 Phillip S Kott 2019-04-11 2019-04-11 13 1 95 101 10.18148/srm/2019.v1i1.7222 Hiding Sensitive Topics by Design? An Experiment on the Reduction of Social Desirability Bias in Factorial Surveys Factorial survey designs have gained increasing popularity within the social sciences. Compared to single-item questions, the method allows the researcher to model more realistic, multidimensional decision scenarios. Furthermore, it has been argued that assessing sensitive dimensions in factorial surveys can help to overcome social desirability bias. One rarely used implementation mode is the between subject design, in which the sensitive dimension varies only between respondents. This method is assumed to attract less attention than a design based on the usual within subject implementation, where respondents see variations on the sensitive dimension among their vignettes. In order to empirically evaluate the between design and its potential to reduce social desirability bias, we conducted an experiment within a general population online survey. Using a split-half design, the sensitive dimension in the vignette texts was either varied within or between subjects. More precisely, the factorial survey module under study assessed respondents’ judgements on just fees for early childcare. Among other dimensions, the vignette texts included the child’s religious denomination (Christian, Muslim, none) as one possible attribute on which discrimination can be based. The split-half approach allows us to compare the widely used within subject design to the alternative between approach. Furthermore, data on respondent characteristics is used to obtain insights about differential design effects for different education groups (differential social desirability bias) and respondents from different religious backgrounds (ingroup favouritism). While results concerning a differential social desirability bias were inconclusive, we found evidence for ingroup favouritism from respondents without a religious denomination in the between condition. In general, our findings suggest that the between subject design is a suitable method for reducing social desirability bias in factorial surveys. Sandra Walzenbach Copyright (c) 2019 Sandra Walzenbach 2019-04-11 2019-04-11 13 1 103 121 10.18148/srm/2019.v1i1.7243 Exploring New Statistical Frontiers at the Intersection of Survey Science and Big Data: Convergence at “BigSurv18” Held in October 2018, The Big Data Meets Survey Science conference, also known as "BigSurv18," provided a first-of-its-kind opportunity for survey researchers, statisticians, computer scientists, and data scientists to convene under the same roof. At this conference, scientists from multiple disciplines were able to exchange ideas about their work might influence and enhance the work of others. This was a landmark event, especially for survey researchers and statisticians, whose industry has been buffeted of late by falling response rates and rising costs at the same time as a proliferation of new tools and techniques, coupled with increasing availability of data, has resulted in "Big Data" approaches to describing and modelling human behavior. Craig A. Hill Paul Biemer Trent Buskirk Mario Callegaro Ana Lucía Córdova Cazar Adam Eck Lilli Japec Antje Kirchner Stas Kolenikov Lars Lyberg Patrick Sturgis Copyright (c) 2019 Craig A Hill 2019-04-11 2019-04-11 13 1 123 134 10.18148/srm/2019.v1i1.7467