Composition of Core Modules and Item Allocation in Split Questionnaire Designs: Impact on Estimates from Imputed Data

Abstract

An increasing number of social science surveys use split questionnaire designs to reduce questionnaire length, presenting only a subset of several questionnaire modules to each respondent while leaving out others. This approach results in large amounts of planned missing data that necessitates imputation. Research shows that imputation is most effective when each module covers various topics. Yet, single-topic modules may sometimes be preferable from a questionnaire-design perspective. A potential alternative from survey practice is using single-topic modules with an extended core module presented to all respondents that includes key items from all topics. This study investigates whether this strategy yields outcomes comparable to mixed-topic modules. Using Monte-Carlo simulations based on the German Internet Panel, we simulate split questionnaire designs, impute the missing data, and calculate estimates based on these data. Findings suggest that while an extended core module improves single-topic module outcomes, it is inferior to randomly allocated mixed-topic modules.