Bayesian analysis for social data: A step-by-step protocol and interpretation

Abstract

The paper proposes Bayesian analysis as an alternative approach for the conventional frequentist approach in analyzing social data. A step-by-step protocol of how to implement Bayesian multilevel model analysis with social data and how to interpret the result is presented. The article used a dataset regarding religious teachings and behaviors of lying and violence as an example. An analysis is performed using R statistical software and a bayesvl R package, which offers a network-structured model construction and visualization power to diagnose and estimate results. • The paper provides guidance for conducting a Bayesian multilevel analysis in social sciences through constructing directed acyclic graphs (DAGs, or ''relationship trees'') for different models, basic and more complex ones. • The method also illustrates how to visualize Bayesian diagnoses and simulated posterior. • The interpretations of visualized diagnoses and simulated posteriors of Bayesian inference are also discussed.

Keywords

• Bayesian statistics
• Social data
• Markov chain monte carlo (MCMC)
• Bayesvl