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- W3162770616 abstract "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." @default.
- W3162770616 created "2021-05-24" @default.
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- W3162770616 date "2021-07-01" @default.
- W3162770616 modified "2023-10-18" @default.
- W3162770616 title "A Primer on Bayesian Model-Averaged Meta-Analysis" @default.
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- W3162770616 doi "https://doi.org/10.1177/25152459211031256" @default.
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