ERIC Number: EJ1109044
Record Type: Journal
Publication Date: 2015-Mar
Abstractor: As Provided
A Bayesian Nonparametric Meta-Analysis Model
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G.
Research Synthesis Methods, v6 n1 p28-44 Mar 2015
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.
Descriptors: Bayesian Statistics, Meta Analysis, Prediction, Nonparametric Statistics, Effect Size, Research Design, Genetics, Scientific Research, Comparative Analysis, Regression (Statistics)
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: SES1156372