Towards Strong Evidence-Based Survey Methodology using Replications, Systematic Reviews, or Bayesian Approaches |
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| Coordinator 1 | Dr Bernd Weiss (GESIS - Leibniz-Institute for the Social Sciences) |
| Coordinator 2 | Ms Jessica Daikeler (GESIS - Leibniz-Institute for the Social Sciences) |
| Coordinator 3 | Dr Henning Silber (GESIS - Leibniz-Institute for the Social Sciences) |
| Coordinator 4 | Professor Michael Bosnjak (Leibniz Institute for Psychology Information) |
The conscientious, explicit and judicious use of current best empirical evidence in making decisions is the central paradigm of the evidence-based practice movement. This movement originated in the medical sciences but found its way in others disciplines like economics, social work and, to a much lesser extent, in survey methodology. The quality of the evidence can be evaluated according to various rating systems that distinguish between different levels of evidence. A common characteristic of these systems is that they value accumulated evidence based on full or partial replications higher than evidence based on a single (and singular) study. Furthermore, within the category of replicative studies, systematic reviews and meta-analysis are considered to promote the best available evidence -- with the gold standard for causal inference being systematic reviews and meta-analyses based on experimental studies. A related, but also overlooked question is how to appropriately incorporate existing evidence in a prospective survey methodological study. Usually, this is done in a qualitative and subjective manner by citing and discussing previous work. However, Bayesian approaches can help to be more rigorous and formal in terms of incorporating past evidence using informative priors, which then are contrasted and updated with the current study and data at hand.
The overall aim of this session is to promote evidence-based survey methodology that is studies aimed at systematically aggregating high-quality evidence on issues relevant for preparing, implementing, and analyzing survey-based research. We encourage the submission of methodological papers, applications, and software/tool demonstrations. Eligible contributions may address, but are not limited to, the following topics:
- Applications and challenges of replicative survey methodology, e.g. issues of pre-registration, determinants of replicability in survey methodology, etc.
- Replicability in the context of big data
- Systematic reviews, gap maps, and meta-analyses in survey methodology
- Using Bayesian approaches to incorporate previous research findings
- Software and tools supporting replications, systematic reviews and meta-analyses or Bayesian approaches in survey methodology contexts