Innovations in Adaptive Survey Designs |
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| Coordinator 1 | Professor Christoph Kern (LMU Munich) |
| Coordinator 2 | Professor Tobias Gummer (GESIS) |
| Coordinator 3 | Dr Bernd Weiß (GESIS) |
| Coordinator 4 | Ms Saskia Bartholomäus (GESIS) |
| Coordinator 5 | Mr John Collins (University of Mannheim) |
Selective participation, low response rates, and decreasing survey engagement threaten the validity of results drawn from survey samples. Against this background, adaptive survey designs are increasingly used to mitigate survey errors in an effort to increase data quality. This design paradigm acknowledges that populations are heterogeneous and, consequently, participation and answering processes differ between population strata. The basic idea behind adaptive survey designs is to vary data collection protocols across these strata to improve key performance indicators (e.g., indicators concerning participation, sample composition, data quality, survey costs). Nonetheless, implementing adaptive designs is challenging as their success critically depends on both correctly identifying different risk groups for treatment and designing and allocating effective treatments.
Modern predictive modeling techniques, carefully designed treatments, and an efficient, tailored allocation of treatments have the potential to innovate the application of adaptive survey designs and improve their effectiveness. Examples include, but are not limited to, the use of machine learning models to identify risk groups, the design of tailored interventions based on the (past or inferred) preferences of survey participants, and the application of modern data-driven methods to study heterogeneous effects of the implemented treatments.
We invite submissions that test the application of adaptive survey designs in social science surveys. Topics of interest include:
• How can adaptive survey design help to reduce nonresponse or measurement errors?
• How can adaptive survey design be used to ease operative efforts required to field a survey and lower survey costs?
• How to leverage advances in predictive modeling when designing and evaluating adaptive designs?
• How to design and select treatments for different sample strata?
• How to identify risk groups requiring different treatment?
• Trade-offs between all the above points