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Making Long Web Surveys Feasible: Matrix Design and Multiple Imputation in Cross-Cultural Surveys

Coordinator 1Dr Michael Ochsner (FORS)
Coordinator 2Dr Gudbjorg Andrea Jonsdottir (University of Iceland)
Coordinator 3Dr Tobias Gummer (GESIS)

Session Details

Cross-cultural surveys are an important instrument for studying change over time and differences in attitudes and values between groups as well as cultures regarding various subjects and topics. To a great extent such large-scale surveys rely on face-to-face interviewing. However, recent developments, such as a more active life style which leads to a lower contact success rate and a much wider internet penetration, make the use of web surveys increasingly promising. Yet, web surveys have the major drawback that the recommended survey length hovers around 20 minutes while cross-cultural general population surveys usually are designed for 60 minutes’ interviews in order to cover a broad range of variables to be analysed together. One way to solve this problem is the application of a matrix design (or split questionnaire design) where the questionnaire is split into modules and not every respondent answers every module. This design can be implemented in various ways, but will result in a data matrix with missing values. Multiple imputation provides one way of analysing these data.
There is still scarce knowledge on how to best apply matrix designs in cross-cultural general population surveys and the methodological implications of doing so. The lack of research is unfortunate since such large-scale surveys are often subject to high costs and require rigid methodological guidelines and standards to ensure cross-national comparability. This session aims at advancing the research on matrix designs and especially welcomes—but is not limited to—submissions that focus on how …
- a matrix can be designed, operated, and implemented in cross-cultural surveys
- feasible the web and other self-administered modes are to conduct general-population surveys with matrix designs
- to detect and resolve issues of cross-cultural comparability when applying matrix designs
- multiple imputation or alternative methods can be applied to analyse data from matrix designs.