Empirical benchmarks for interpreting effect size variability in meta-analysis

Wiernik, B. M., Kostal. J. W., Wilmot, M. P., Dilchert, S.,  & Ones, D. S.
Industrial and Organizational Psychology, 10(3), 472–479
(2017) https://doi.org/10/ccnv

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.