Paper Title
A comparison of methods to test mediation and other intervening variable effects
Keywords
- Mediation Testing
- Intervening Variable Effects
- Monte Carlo Study
- Statistical Significance
- Baron and Kenny Approach
- Type I Error Rates
- Statistical Power
- Distribution of the Product
- Difference-in-Coefficients Methods
- Joint Significance Test
- Mediation Analysis
- Statistical Testing Methods
- Mediator Variables
- Research Methodology
Journal
Research Impact Tools
Publication Info
Volume: 7 | Issue: 1 | Pages: 83–104
Published On
March, 2002
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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.
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