Empirical Benchmarks for Interpreting Effect Size Variability in Meta-Analysis

Brenton M. Wiernik, Jack W. Kostal, Michael P. Wilmot, Stephan Dilchert and Deniz S. Ones

Industrial and Organizational Psychology, 2017, vol. 10, issue 3, 472-479

Abstract: Generalization in meta-analyses is not a dichotomous decision (typically encountered in papers using the Q test for homogeneity, the 75% rule, or null hypothesis tests). Inattention to effect size variability in meta-analyses may stem from a lack of guidelines for interpreting credibility intervals. In this commentary, we describe two methods for making practical interpretations and determining whether a particular SDρ represents a meaningful level of variability.

Date: 2017
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