  In a meta-analysis, it is important to specify a model that adequately
describes the effect-size distribution of the underlying population of studies.
The conventional normal fixed-effect and normal random-effects models assume a
normal effect-size population distribution, conditionally on parameters and
covariates. For estimating the mean overall effect size, such models may be
adequate, but for prediction they surely are not if the effect size
distribution exhibits non-normal behavior. To address this issue, we propose a
Bayesian nonparametric meta-analysis model, which can describe a wider range of
effect-size distributions, including unimodal symmetric distributions, as well
as skewed and more multimodal distributions. We demonstrate our model through
the analysis of real meta-analytic data arising from behavioral-genetic
research. We compare the predictive performance of the Bayesian nonparametric
model against various conventional and more modern normal fixed-effects and
random-effects models.
