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Treating Uncertainty in Meta-Analytic Results

Published online by Cambridge University Press:  30 August 2017

Michael T. Brannick
Michael T. Brannick*
Affiliation:
Psychology Department, University of South Florida
*
Correspondence concerning this article should be addressed to Michael T. Brannick, Psychology Department, PCD 4118G, University of South Florida, Tampa, FL 33620. E-mail: [email protected]
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Extract

Tett, Hundley, and Christiansen (2017) describe two main sources of uncertainty that are usually reported in a meta-analysis: uncertainty about the value of the underlying mean correlation (which they describe as SE(rxy)) and uncertainty about the individual values of rho that arise from the random-effects variance (the square root of which they describe as SD(rho)). They proceed to recommend descriptions of small, medium, and large values of each uncertainty that meta-analysts should report and consider for interpretation. However, there exists a simpler solution to expressing and interpreting such uncertainty.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 10 , Issue 3 , September 2017 , pp. 457 - 459
Copyright
Copyright © Society for Industrial and Organizational Psychology 2017 

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References

Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009). Introduction to meta-analysis. West Sussex, UK: Wiley.CrossRefGoogle Scholar
Oswald, F. L., & McCloy, R. A. (2003). Meta-analysis and the art of the average. In Murphy, K. (Ed.), Validity generalization: A critical review (pp. 311–338). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Paterson, T. A., Harms, P. D., Steel, P., & Crede, 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. doi:10.1177/1548051815614321 Google Scholar
Schmidt, F. L., & Hunter, J. E. (2015). Methods of meta-analysis: Correcting error and bias in research findings (3rd ed.). Thousand Oaks, CA: Sage.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
Whitener, E. M. (1990). Confusion of confidence intervals and credibility intervals in meta-analysis. Journal of Applied Psychology, 75, 315–321.CrossRefGoogle Scholar