Meta-analysis is the predominant approach for quantitatively synthesizing a set of studies. If the studies themselves are of high quality, meta-analysis can provide valuable insights into the current scientific state of knowledge about a particular phenomenon. In psychological science, the most common approach is to conduct frequentist meta-analysis. In this primer, we discuss an alternative method, Bayesian model-averaged meta-analysis. This procedure combines the results of four Bayesian meta-analysis models: (a) fixed-effect null hypothesis, (b) fixed-effect alternative hypothesis, (c) random-effects null hypothesis, and (d) random-effects alternative hypothesis. These models are combined according to their plausibilities given the observed data to address the two key questions “Is the overall effect nonzero?” and “Is there between-study variability in effect size?” Bayesian model-averaged meta-analysis therefore avoids the need to select either a fixed-effect or random-effects model and instead takes into account model uncertainty in a principled manner.
A Primer on Bayesian Model-Averaged Meta-Analysis
Q. Gronau,D. Heck,S. Berkhout,J. Haaf,E. Wagenmakers
Published 2020 in Advances in Methods and Practices in Psychological Science
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- Publication year
2020
- Venue
Advances in Methods and Practices in Psychological Science
- Publication date
2020-04-24
- Fields of study
Computer Science, Psychology
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