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Best-practice recommendations for estimating interaction effects using meta-analysis

Authors

  • Herman Aguinis,

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

    1. Department of Management, 215 Calvin Hall, Kansas State University, Manhattan, Kansas 66506, U.S.A.
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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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