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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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References

Berry, C. M., Ones, D. S., & Sackett, P. R. (2007). Interpersonal deviance, organizational deviance, and their common correlates: A review and meta-analysis. Journal of Applied Psychology, 92 (2), 410–424. https://doi.org/10.1037/0021-9010.92.2.410 Google Scholar
Blume, B. D., Ford, J. K., Baldwin, T. T., & Huang, J. L. (2010). Transfer of training: A meta-analytic review. Journal of Management, 36 (4), 1065–1105. https://doi.org/10/ftwgxk Google Scholar
Bosco, F. A., Aguinis, H., Singh, K., Field, J. G., & Pierce, C. A. (2015). Correlational effect size benchmarks. Journal of Applied Psychology, 100 (2), 431–449. http://doi.org/10/bnw8 Google Scholar
Chang, C.-H., Ferris, D. L., Johnson, R. E., Rosen, C. C., & Tan, J. A. (2012). Core self-evaluations: A review and evaluation of the literature. Journal of Management, 38 (1), 81–128. https://doi.org/10/dbd9nb Google Scholar
Chiaburu, D. S., Oh, I.-S., Berry, C. M., Li, N., & Gardner, R. G. (2011). The five-factor model of personality traits and organizational citizenship behaviors: A meta-analysis. Journal of Applied Psychology, 96 (6), 1140–1166. https://doi.org/10.1037/a0024004 Google Scholar
Christian, M. S., Garza, A. S., & Slaughter, J. E. (2011). Work engagement: A quantitative review and test of its relations with task and contextual performance. Personnel Psychology, 64 (1), 89–136. https://doi.org/10/c6b58z Google Scholar
Credé, M., Tynan, M. C., & Harms, P. D. (2016). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology. Advance online publication. https://doi.org/10.1037/pspp0000102 Google Scholar
Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and Individual Differences, 102, 74–78. http://doi.org/10/f84bhv CrossRefGoogle Scholar
Hemphill, J. F. (2003). Interpreting the magnitudes of correlation coefficients. American Psychologist, 58 (1), 78–79. http://doi.org/10/fb38g8 Google Scholar
Judge, T. A., Heller, D., & Mount, M. K. (2002). Five-factor model of personality and job satisfaction: A meta-analysis. Journal of Applied Psychology, 87 (3), 530–541. https://doi.org/10.1037//0021-9010.87.3.530 Google Scholar
Judge, T. A., Rodell, J. B., Klinger, R. L., Simon, L. S., & Crawford, E. R. (2013). Hierarchical representations of the five-factor model of personality in predicting job performance: Integrating three organizing frameworks with two theoretical perspectives. Journal of Applied Psychology, 98 (6), 875–925. https://doi.org/10/bdbb Google Scholar
Ng, T. W. H., Eby, L. T., Sorensen, K. L., & Feldman, D. C. (2005). Predictors of objective and subjective career success: A meta-analysis. Personnel Psychology, 58 (2), 367–408. https://doi.org/10/dw64z6 Google Scholar
Paterson, T. A., Harms, P. D., Steel, P., & Credé, M. (2016). An assessment of the magnitude of effect sizes: Evidence from 30 years of meta-analysis in management. Journal of Leadership & Organizational Studies, 23 (1), 66–81. http://doi.org/10/bjz9 Google Scholar
Raju, N. S., Burke, M. J., Normand, J., & Langlois, G. M. (1991). A new meta-analytic approach. Journal of Applied Psychology, 76 (3), 432–446. http://doi.org/10/dcrgkf Google Scholar
Schmidt, F. L. (2008). Meta-analysis: A constantly evolving research integration tool. Organizational Research Methods, 11 (1), 96–113. http://doi.org/10/drwrb2 CrossRefGoogle Scholar
Steel, P. D. G. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133 (1), 65–94. https://doi.org/10.1037/0033-2909.133.1.65 Google Scholar
Steel, P., Schmidt, J., & Shultz, J. (2008). Refining the relationship between personality and subjective well-being. Psychological Bulletin, 134 (1), 138–161.Google Scholar
Steel, P. D. G., Kammeyer-Mueller, J., & Paterson, T. A. (2015). Improving the meta-analytic assessment of effect size variance with an informed Bayesian prior. Journal of Management, 41 (2), 718–743. http://doi.org/10/b6rc Google Scholar
Tett, R. P., Hundley, N. A., & Christiansen, N. D. (2017). Meta-analysis and the myth of generalizability. Industrial and Organizational Psychology: Perspectives on Science and Practice, 10 (3), 421–456.Google Scholar
Thomas, J. P., Whitman, D. S., & Viswesvaran, C. (2010). Employee proactivity in organizations: A comparative meta-analysis of emergent proactive constructs. Journal of Occupational and Organizational Psychology, 83 (2), 275–300. https://doi.org/10.1348/096317910x502359 Google Scholar
Vacha-Haase, T., Tani, C. R., Kogan, L. R., Woodall, R. A., & Thompson, B. (2001). Reliability generalization: Exploring reliability variations on MMPI/MMPI-2 validity scale scores. Assessment, 8 (4), 391–401. http://doi.org/10.1177/107319110100800404 Google Scholar
Wilmot, M. P. (2017). Personality and its impacts across the behavioral sciences: A quantitative review of meta-analytic findings. Doctoral dissertation. University of Minnesota, Minneapolis, MN.Google Scholar
Zimmerman, R. D. (2008). Understanding the impact of personality traits on individuals' turnover decisions: A meta-analytic path model. Personnel Psychology, 61 (2), 309–348. https://doi.org/10.1111/j.1744-6570.2008.00115.x Google Scholar