Bayesian analysis for social data: A step-by-step protocol and interpretation
Quan‐Hoang Vuong, Viet‐Phuong La, Minh‐Hoang Nguyen, Manh‐Toan Ho, Trung Tran, Tung Manh Ho
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.