Developing Innovative and Model-Based Interventions to Combat Survey Satisficing |
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| Coordinator 1 | Ms Julia Witton (German Institute for Economic Research) |
| Coordinator 2 | Dr Carina Cornesse (Free University of Berlin and German Institute for Economic Research) |
The concept of satisficing was introduced to the survey context by Krosnick in 1991. Since then, it has kept researchers busy and remains one of the most important contributors to total survey error. Respondents exhibit problematic response behaviors such as midpoint or endpoint selection, nondifferentiation, or item nonresponse, aiming to reduce the cognitive burden that the survey response process entails. These response strategies evidently impair the quality of the generated data, which in turn affects the validity and reliability of the findings that researchers derive from this data. In times of increasing financial costs and decreasing response rates, it is particularly necessary to maximize quality when collecting survey data. Numerous theoretical insights were gained in the past decades, and there are some general recommendations to prevent participants from satisficing. While in several domains of survey data collection, innovative interventions to maximize the collected data’s quality (e.g., targeted data collection mode assignment) have become increasingly relevant, innovation on measures that address the prevention of satisficing is still in its infancy. There are, in fact, some promising results with targeted interventions such as prompting participants when speeding is detected or when they try to skip a question before answering it.
In this session, we want to shed light on current developments in the field that help to innovate the survey landscape’s handling of satisficing. We aim to put a spotlight on the practical aspects and welcome contributions that address existing challenges of satisficing in longitudinal as well as cross-sectional data collection settings. We are particularly interested in the following contributions:
- Interventions based on statistical models (e.g. latent class analyses or prediction models)
- Experimental assessments of satisficing interventions (e.g. split-ballots)
- Longitudinal approaches to measuring and combating satisficing