Bayesian mediation analysis in trauma research

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.

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.

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).

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).