title: A comparison of methods to test mediation and other intervening variable effects.
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.
authors.affiliation: Department of Psychology, Arizona State University, Tempe 85287-1104, USA. David.MacKinnon@asu.edu
authors.forename: David P
authors.initials: DP
authors.lastname: MacKinnon
authors.forename: Chondra M
authors.initials: CM
authors.lastname: Lockwood
authors.forename: Jeanne M
authors.initials: JM
authors.lastname: Hoffman
authors.forename: Stephen G
authors.initials: SG
authors.lastname: West
authors.forename: Virgil
authors.initials: V
authors.lastname: Sheets
mesh_terms: D006801:Humans; D008960:Models, Psychological