A comparison of methods to test mediation and other intervening variable effects.

TLDR

An intervening variable transmits the effect of an independent variable to a dependent variable, but the classic Baron and Kenny (1986) approach has low statistical power. A Monte Carlo study compared 14 methods for testing the statistical significance of the intervening variable effect. Two product‑distribution and two difference‑in‑coefficients methods yield the most accurate Type I error rates and greatest power, except in one case where error rates are too high, and the joint‑significance test of the two effects provides the best overall balance of error and power.

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.