The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Best‐practice recommendations for estimating interaction effects using meta‐analysis
Abstract
One of the key advantages of meta‐analysis (i.e., a quantitative literature review) over a narrative literature review is that it allows for formal tests of interaction effects—namely, whether the relationship between two variables is contingent upon the value of another (moderator) variable. Interaction effects play a central role in organizational science research because they highlight boundary conditions of a theory: Conditions under which relationships change in strength and/or direction. This article describes procedures for estimating interaction effects using meta‐analysis, distills the technical literature for a general readership of organizational science researchers, and includes specific best‐practice recommendations regarding actions researchers can take before and after data collection to improve the accuracy of substantive conclusions regarding interaction effects investigated meta‐analytically. Copyright © 2010 John Wiley & Sons, Ltd.
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The power of statistical tests for moderators in meta-analysis
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Metadata
- Authors
- Aguinis, H., Gottfredson, R. K., & Wright, T. A.
- Venue
- Journal of Organizational Behavior
- Publisher
- Wiley
- Published
- 2011-10-13
- Volume
- 32
- Issue
- 8
- Pages
- 1033-1043
- ISSN
- 0894-3796 (ISSN-L)
- 1099-1379
- OpenAlex
- W2063602140
- Cited by
- 198
- References
- 11
- FWCI
- 6.62
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
