Empirical Benchmarks for Interpreting Effect Size Variability in Meta-Analysis

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....

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Bibliographic Details
Published in:Industrial and organizational psychology 2017-09, Vol.10 (3), p.472-479
Main Authors: Wiernik, Brenton M., Kostal, Jack W., Wilmot, Michael P., Dilchert, Stephan, Ones, Deniz S.
Format: Article
Language:English
Subjects:
Commentaries
Credibility
Economic models
Estimates
Meta-analysis
Personality
Psychology
Research methodology
Systematic review
Variables
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Summary: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.
ISSN:1754-9426
1754-9434
DOI:10.1017/iop.2017.44