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