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.

Keywords

Type I and type II errorsStatisticsStatistical significanceStatistical hypothesis testingVariable (mathematics)Statistical powerVariablesMediationMonte Carlo methodMathematicsEconometrics

MeSH Terms

HumansModelsPsychological

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Publication Info

Year
2002
Type
article
Volume
7
Issue
1
Pages
83-104
Citations
8945
Access
Closed

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Cite This

David P. MacKinnon, Chondra M. Lockwood, Jeanne M. Hoffman et al. (2002). A comparison of methods to test mediation and other intervening variable effects.. Psychological Methods , 7 (1) , 83-104. https://doi.org/10.1037/1082-989x.7.1.83

Identifiers

DOI
10.1037/1082-989x.7.1.83
PMID
11928892
PMCID
PMC2819363

Data Quality

Data completeness: 86%