A comparison of methods to test mediation and other intervening variable effects.
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A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect and found 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.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.read more
Citations
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Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models
TL;DR: An overview of simple and multiple mediation is provided and three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model are explored.
Journal ArticleDOI
SPSS and SAS procedures for estimating indirect effects in simple mediation models.
TL;DR: It is argued the importance of directly testing the significance of indirect effects and provided SPSS and SAS macros that facilitate estimation of the indirect effect with a normal theory approach and a bootstrap approach to obtaining confidence intervals to enhance the frequency of formal mediation tests in the psychology literature.
Journal ArticleDOI
Mediation in experimental and nonexperimental studies: New procedures and recommendations.
Patrick E. Shrout,Niall Bolger +1 more
TL;DR: Efron and Tibshirani as discussed by the authors used bootstrap tests to assess mediation, finding that the sampling distribution of the mediated effect is skewed away from 0, and they argued that R. M. Kenny's (1986) recommendation of first testing the X --> Y association for statistical significance should not be a requirement when there is a priori belief that the effect size is small or suppression is a possibility.
Journal ArticleDOI
Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis
TL;DR: Baron and Kenny's procedure for determining if an independent variable affects a dependent variable through some mediator is so well known that it is used by authors and requested by reviewers almost reflexively.
Journal ArticleDOI
Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions.
TL;DR: This article disentangle conflicting definitions of moderated mediation and describes approaches for estimating and testing a variety of hypotheses involving conditional indirect effects, showing that the indirect effect of intrinsic student interest on mathematics performance through teacher perceptions of talent is moderated by student math self-concept.
References
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