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Empirical Benchmarks for Interpreting Effect Size Variability in Meta-Analysis

Published online by Cambridge University Press:  30 August 2017

Brenton M. Wiernik ,
Jack W. Kostal ,
Michael P. Wilmot ,
Stephan Dilchert  and
Deniz S. Ones
Brenton M. Wiernik*
Affiliation:
Department of Developmental, Personality and Social Psychology, Ghent University
Jack W. Kostal
Affiliation:
Department of Psychology, University of Minnesota
Michael P. Wilmot
Affiliation:
Department of Psychology, University of Minnesota
Stephan Dilchert
Affiliation:
Narendra Paul Loomba Department of Management, Baruch College, CUNY
Deniz S. Ones
Affiliation:
Department of Psychology, University of Minnesota
*
Correspondence concerning this article should be addressed to Brenton M. Wiernik, Department of Developmental, Personality and Social Psychology, Ghent University, Henri Dunantlaan 2, 9000 Gent, Belgium. Email: wiernik@workpsy.ch
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Extract

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.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 10 , Issue 3 , September 2017 , pp. 472 - 479
Copyright
Copyright © Society for Industrial and Organizational Psychology 2017 

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