Objective: Bayesian methods are growing in popularity among social scientists, due to the significant advantages offered to researchers: namely, intuitive probabilistic interpretations of results. Here, we highlight the benefits of using the Bayesian framework in research where collecting large samples is challenging, specifically: the absence of a requirement of large samples for convergence, and the possibility of building on prior research by including informative priors.
Method: We demonstrate how to fit a single mediator model and impute missing data in the Bayesian framework using the software JAGS via the R package rjags. To this end, we use open-access data to fit a mediation model and calculate the posterior probability that the mediated effect is above a specified criterion.
Results: We replicate the results of the original paper in the Bayesian framework and provide annotated code for mediation analysis in rjags, as well as two additional R packages for Bayesian analysis (brms and rstan) and two additional software packages (SAS and Mplus).
Conclusion: We provide guidelines for reporting and interpreting results obtained in the Bayesian framework, and two extensions to the mediation model are discussed: adding covariates to the model and selecting informative priors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).