A default Bayesian hypothesis test for mediation

In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent var...

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Bibliographic Details
Published in:Behavior research methods 2015-03, Vol.47 (1), p.85-97
Main Authors: Nuijten, Michèle B., Wetzels, Ruud, Matzke, Dora, Dolan, Conor V., Wagenmakers, Eric-Jan
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Behavioral Science and Psychology
Cognitive Psychology
Humans
Hypothesis testing
Markov Chains
Mediation
Monte Carlo Method
Multivariate Analysis
Negotiating
Parameter estimation
Psychology
Research Design
Research methodology
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Summary:In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon ( Psychological Methods, 14, 301–322, 2009 ), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys–Zellner–Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014 ).
ISSN:1554-3528
1554-3528
DOI:10.3758/s13428-014-0470-2