Best-practice recommendations for estimating interaction effects using moderated multiple regression

An interaction effect indicates that a relationship is contingent upon the values of another (moderator) variable. Thus, interaction effects describe conditions under which relationships change in strength and/or direction. Understanding interaction effects is essential for the advancement of the or...

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Bibliographic Details
Published in:Journal of organizational behavior 2010-08, Vol.31 (6), p.776-786
Main Authors: Aguinis, Herman, Gottfredson, Ryan K.
Format: Article
Language:English
Subjects:
Accuracy
Applied psychology
Best practice
Design analysis
Moderated
Moderator variables
Multiple regression
Null hypothesis
Organization theory
Organizational behavior
Population estimates
Readership
Recommendations
Regression analysis
Research methodology
Research methods
Researcher's Notebook
Sample size
Statistical variance
Studies
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Summary:An interaction effect indicates that a relationship is contingent upon the values of another (moderator) variable. Thus, interaction effects describe conditions under which relationships change in strength and/or direction. Understanding interaction effects is essential for the advancement of the organizational sciences because they highlight a theory's boundary conditions. We describe procedures for estimating and interpreting interaction effects using moderated multiple regression (MMR). We distill the technical literature for a general readership of organizational science researchers and include specific best-practice recommendations regarding actions researchers can take before and after data collection to improve the accuracy of MMR-based conclusions regarding interaction effects.
ISSN:0894-3796
1099-1379
DOI:10.1002/job.686