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 statistic...

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Bibliographic Details
Published in:Psychological methods 2002-03, Vol.7 (1), p.83-104
Main Authors: MacKinnon, David P, Lockwood, Chondra M, Hoffman, Jeanne M, West, Stephen G, Sheets, Virgil
Format: Article
Language:English
Subjects:
Biological and medical sciences
Dependent Variables
Fundamental and applied biological sciences. Psychology
Humans
Independent Variables
Models, Psychological
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics. Statistics. Methodology
Statistical Power
Statistical Significance
Statistical Variables
Statistics. Mathematics
Type I Errors
Citations: Items that this one cites
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Summary: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.
ISSN:1082-989X
1939-1463
DOI:10.1037/1082-989X.7.1.83