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A Prior Distribution for Bayesian Meta Analyses
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Description: We create a data-driven prior distribution for meta-analyses in social psychology. We also outline a method that researchers can use to develop priors for their own field.
Bayesian methods can be effective for conducting meta-analyses as they allow researchers to integrate their own predictions into the analysis. However, researchers may struggle to decide which prior distribution is appropriate. To help researchers in this pursuit, we propose a data-driven method to develop prior distributions. As a proof of concept, we constructed three prior distributions based o…
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