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A comparison of methods to test mediation and other intervening variable effects
David P MacKinnon1, Chondra M Lockwood, Jeanne M Hoffman
1Department of Psychology, Arizona State University, Tempe 85287-1104, USA. David.MacKinnon@asu.edu
Abstract:
A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect. An intervening variable (mediator) transmits the effect of an independent variable to a dependent variable. The commonly used R. M. Baron and D. A. Kenny (1986) approach has low statistical power. Two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power except in 1 important case in which Type I error rates are too high. The best balance of Type I error and statistical power across all cases is the test of the joint significance of the two effects comprising the intervening variable effect.