Responsive and Adaptive Surveys: Are they really addressing current Data Collection challenges? |
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| Coordinator 1 | Dr Dimitri Prandner (Johannes Kepler University of Linz) |
| Coordinator 2 | Professor Patrick Kutschar (Paracelsus Medical University) |
| Coordinator 3 | Professor Martin Weichbold (Paris Lodron University of Salzburg) |
| Coordinator 4 | Mr Christopher Etter (Paris Lodron University of Salzburg) |
Recent evidence challenges the longstanding reliance on rigid, single-mode surveys. Individual differences in survey participation motivation, preferred survey modes or specific question formats suggest the need for more flexible, participant-tailored approaches. Thus, responsive and adaptive designs (RAD), that allow for the use of various sampling and surveying methods tailored to different populations, survey topics, and data collection contexts gained traction over the last few years.
Methodological research has consistently shown that pre-planned conditional adaptive survey paths and situational, dynamically adjusting data collection procedures can improve both cost efficiency and data quality. It is often argued that using RAD to adapt survey methods in real-time to optimally align design features with respondent characteristics improves measurement quality overall.
However, while RAD can mitigate certain sources of error and bias, it has also been noted that it may introduce additional new ones. Using the total survey error (TSE) framework, RAD-related design trade-offs can impact various error sources from both TSE components: representation (e.g., refusals, nonresponse) and measurement (e.g., interviewer effects, context effects).
We invite theoretical, conceptual, and empirical papers from laboratory and field research (small to large scale) that address the implications of RAD for data quality. Topics of interest include, but are not limited to:
• Tailored contact strategies and survey modes (e.g., integration of RAD and push-to-web approaches, individualized incentives)
• Adaptive changes to design features during the interview (e.g., proxies, mixed mode/methods, instruments, question difficulty, format or layout, visuals and pictures)
• Predictors for and RAD application in certain respondent groups and specific populations (e.g., vulnerable populations)
• The role of advanced technologies in real-time data monitoring and adjustment (e.g., AI-assisted adaptive procedures, machine learning)
• The use of auxiliary data to inform adaptive survey design
• Strategies and