Open AccessA Primer on Bayesian Model-Averaged Meta-Analysis
Author(s) -
Quentin Frederik Gronau,
Daniel W. Heck,
Sophie W. Berkhout,
Julia M. Haaf,
EricJan Wagenmakers
Publication year - 2021
Publication title -
advances in methods and practices in psychological science
Language(s) - English
Resource type - Journals
eISSN - 2515-2467
pISSN - 2515-2459
DOI - 10.1177/25152459211031256
Subject(s) - frequentist inference , random effects model , meta analysis , bayesian probability , fixed effects model , econometrics , null hypothesis , bayesian statistics , computer science , bayesian inference
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.