Modular Questionnaire Designs for Social Surveys: Statistical Modelling of Designed Missingness

Research question/goal: 

Surveys have become an indispensable source of information on social and political circumstances in modern societies. Quantitative social research based on survey data requires ever larger data sets containing ever more complex structures. Together with decreasing response rates and increasing fieldwork efforts, the heightened expectations regarding data quality lead to surging survey costs.

Fortunately, the developments in statistical modelling and associated computing power have seen large developments in the past twenty years, enabling us to rethink traditional survey data collection methods. In particular, two developments seem promising: modular (or split) questionnaires and imputation methods.

The project aims to investigate whether these methods can be combined and further developed to replace large-scale face-to-face surveys by shorter online surveys while preserving the same degree of population coverage and quality. This project is a first step in developing and evaluating the necessary statistical tools to complement data structures collected by modular questionnaire designs. The main interest lies in assessing the estimation efficiency and bias of imputation methods. Further considerations concern the potential for cost savings and usability.

In the first phase of the project, data sets of the waves of the German Internet Panel are used to evaluate the approaches. In the second phase, we will analyse and impute datasets from modular questionnaire designs, implemented in the European Value Survey. Resulting data sets are imputed and analysed regarding the aim of the project.

Current stage: 

In 2022, we extended our simulation studies of planned missing data from modular questionnaire designs by additional missing data from item nonresponse among the survey participants. A paper on these simulations is being prepared and will be submitted to a journal in 2023. Moreover, two papers on previous findings from the project have been published in the Journal for Survey Statistics and Methodology and Statistics Surveys. Key findings from the project were also presented at the 2022 conference of the “Deutsche Arbeitsgemeinschaft Statistik” (DAGStat) and at the symposium of the “High Performance Computing, Data Intensive Computing and Large Scale Scientific Data Management in Baden-Württemberg” initiative (bwHPC).

Fact sheet

2017 to 2023
Data Sources: 
German Internet Panel, European Value Study
Geographic Space: