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Bayesian methods offer intuitive results for social scientists, especially when large samples are difficult. This study demonstrates Bayesian mediation analysis, providing code and guidelines for its application.

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Area of Science:

  • Social Sciences
  • Statistics

Background:

  • Bayesian methods are increasingly adopted in social sciences for their probabilistic interpretations.
  • The Bayesian framework is advantageous for research with limited sample sizes, offering convergence without large datasets.
  • It allows for the incorporation of prior research through informative priors.

Purpose of the Study:

  • To highlight the benefits of the Bayesian framework for social science research, particularly with small sample sizes.
  • To demonstrate the application of Bayesian methods for mediation analysis and missing data imputation.
  • To provide practical guidance and code for implementing Bayesian mediation analysis.

Main Methods:

  • A single mediator model was fitted using Bayesian inference with the JAGS software via the rjags R package.
  • Missing data imputation was performed within the Bayesian framework.
  • Posterior probabilities for the mediated effect were calculated.

Main Results:

  • The study successfully replicated findings from a previous paper using the Bayesian framework.
  • Annotated code for mediation analysis was provided for rjags, brms, rstan, SAS, and Mplus.
  • The analysis demonstrated the feasibility of Bayesian mediation analysis with available software.

Conclusions:

  • Guidelines for reporting and interpreting Bayesian analysis results are presented.
  • Extensions to the mediation model, including adding covariates and using informative priors, are discussed.
  • The study facilitates the adoption of Bayesian methods in social science research.