A group-specific prior distribution for effect-size heterogeneity in meta-analysis

Abstract

While both methodological and applied work on Bayesian meta-analysis have flourished, Bayesian modeling of differences between groups of studies remains scarce in meta-analyses in psychology, education, and the social sciences. On rare occasions when Bayesian approaches have been used, non-informative prior distributions have been chosen. However, more informative prior distributions have recently garnered popularity. We propose a group-specific weakly informative prior distribution for the between-studies standard-deviation parameter in meta-analysis. The proposed prior distribution incorporates a frequentist estimate of the between-studies standard deviation as the noncentrality parameter in a folded noncentral t distribution. This prior distribution is then separately modeled for each subgroup of studies, as determined by a categorical factor. Use of the new prior distribution is shown in two extensive examples based on a published meta-analysis on psychological interventions aimed at increasing optimism. We compare the folded noncentral t prior distribution to several non-informative prior distributions. We conclude with discussion, limitations, and avenues for further development of Bayesian meta-analysis in psychology and the social sciences.