Moderation in Management Research: What, Why, When, and How
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Many theories in management, psychology, and other disciplines rely on moderating variables: those which affect the strength or nature of the relationship between two other variables. Despite the near-ubiquitous nature of such effects, the methods for testing and interpreting them are not always well understood. This article introduces the concept of moderation and describes how moderator effects are tested and interpreted for a series of model types, beginning with straightforward two-way interactions with Normal outcomes, moving to three-way and curvilinear interactions, and then to models with non-Normal outcomes including binary logistic regression and Poisson regression. In particular, methods of interpreting and probing these latter model types, such as simple slope analysis and slope difference tests, are described. It then gives answers to twelve frequently asked questions about testing and interpreting moderator effects.
KeywordsModeration Interactions Simple slopes Regression
I am grateful to Ron Landis, Scott Tonidandel, and Steven Rogelberg for their comments on earlier drafts of this article.
- Aguinis, H. (1995). Statistical power problems with moderated regression in management research. Journal of Management, 21, 1141–1158.Google Scholar
- Aguinis, H. (2004). Regression analysis for categorical moderators. New York: Guilford Press.Google Scholar
- Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, London: Sage.Google Scholar
- Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.Google Scholar
- Landis, R. S. (2013). Successfully combining meta-analysis and structural equation modeling: Recommendations and strategies. Journal of Business and Psychology. doi: 10.1007/s10869-013-9285-x.
- Mirisola, A., & Seta, L. (2013). pequod: Moderated regression package. R package version 0.0-3 [Computer software]. Retrieved March 20, 2013. Available from http://CRAN.R-project.org/package=pequod.
- Muthén, L. K., & Muthén, B. O. (1998-2011). Mplus user’s guide (6th ed). Los Angeles, CA: Muthén & Muthén.Google Scholar
- Rutherford, A. (2001). Introducing ANOVA and ANCOVA: A GLM approach. London: Sage.Google Scholar
- Shanock, L. R., Baran, B. E., Gentry, W. A., Pattison, S. C., & Heggestad, E. D. (2010). Polynomial regression with response surface analysis: A powerful approach for examining moderation and overcoming limitations of difference scores. Journal of Business and Psychology, 25, 543–554.CrossRefGoogle Scholar
- Snijders, T., & Bosker, R. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
- West, B., Welch, K. B., & Galecki, A. T. (2007). Linear mixed models: A practical guide using statistical software. Boca Raton, FL: Chapman & Hall.Google Scholar