An introductory examination on the differences between frequentist and Bayesian multiple regression using real-world data on bias-based victimization among Latinx adults.

Abstract

The aim of the current paper is to provide an applied introduction and overview of Bayesian methodology, how it compares from commonly used frequentist methods, and to elaborate on the utility of Bayesian methods in trauma and mental health research. Using data from the second wave of the Longitudinal Examination of Victimization Experiences of Latinos (LEVEL) study (N = 323) we ran frequentist modeling using OLS regression to test the effects of lifetime victimization, hate crime, and noncriminal bias events on anxiety, depression, anger, and dissociation. For the Bayesian analyses, we replicate these regressions using both weakly informative and highly informative priors, as well as a likelihood function that addresses data skew. Results across the 3 analyses present some key differences. In the frequentist models we find that lifetime victimization, hate crime, and noncriminal bias events had significant and positive relationship with anxiety, depression, and anger. Only hate crimes were significantly related to dissociation. The Bayesian results change based on which priors were implemented into the models. Ultimately, the results differ both across methodologies and within the Bayesian methodology depending on type of prior used. Several meaningful differences between the approaches emerge resulting in different interpretations of these results. Bayesian analyses serve as an additional tool for researchers that can be used to answer new and unique research questions that may be inaccessible by frequentist methods. (PsycInfo Database Record (c) 2022 APA, all rights reserved).