The authors provide a description of their analysis of how Bayesian methodology compares with frequentist methods for trauma and mental health research; they report on their research methodology, outcomes, and implications for future research.
The aim of this 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, the authors 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, the authors replicated these regressions using both weakly informative and highly informative priors, as well as a likelihood function that addresses data skew. Results across the three analyses presented 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 changed based on which priors were implemented into the models. Ultimately, the results differed both across methodologies and within the Bayesian methodology depending on type of prior used. Several meaningful differences between the approaches emerged, 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. Publisher Abstract Provided