A comparison of methods to test mediation and other intervening variable effects.
David P. MacKinnonDepartment of Psychology, Arizona State University, Tempe 85287-1104, USA. [email protected]Chondra M. LockwoodArizona State U, Dept of Psychology, Tempe, AZ, USJeanne M. HoffmanArizona State U, Dept of Psychology, Tempe, AZ, USStephen G. WestArizona State U, Dept of Psychology, Tempe, AZ, USVirgil L. SheetsArizona State U, Dept of Psychology, Tempe, AZ, US
2002en
ABI
Аннотация
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.
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