Hostname: page-component-76fb5796d-25wd4 Total loading time: 0 Render date: 2024-04-25T17:11:14.261Z Has data issue: false hasContentIssue false

Treating Uncertainty in Meta-Analytic Results

Published online by Cambridge University Press:  30 August 2017

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: mbrannic@usf.edu

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
Copyright
Copyright © Society for Industrial and Organizational Psychology 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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. 311338). 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), 6681. 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, 315321.CrossRefGoogle Scholar