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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.