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

Authors

  • Herman Aguinis,

    Corresponding author
    1. Department of Management & Entrepreneurship, Kelley School of Business, Indiana University, Bloomington, Indiana, U.S.A.
    • Department of Management & Entrepreneurship, Kelley School of Business, Indiana University, 1309 E. 10th Street, Bloomington, IN 47405-1701, U.S.A.
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  • Ryan K. Gottfredson

    1. Department of Management & Entrepreneurship, Kelley School of Business, Indiana University, Bloomington, Indiana, U.S.A.
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Abstract

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. Copyright © 2010 John Wiley & Sons, Ltd.

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