Using Bayesian Meta-Regression to Advance Prevention Science Research: an Introduction and Empirical Illustration.

Driven by the movement of evidence-based practices, Bayesian statistical methods have become increasingly popular. This paper introduces a Bayesian approach to meta-regression, focusing on the use and implementation in prevention science research. We first compare Bayesian meta-analysis and meta-regression to a frequentist approach. Thereafter, we illustrate Bayesian methods in meta-regression, highlighting advantages, providing detailed interpretation, and presenting results. The example is completed using several R packages. We also provide annotated R code for readers as a foundation for their own research.

Eudaimonic Well-Being for Lesbian and Bisexual Women: The Roles of Religion and Social Connectedness.

Understanding how religion and well-being are related for lesbian and bisexual women provides important context for clinical interventions. Current literature in the field diverges on whether the relationship between religious commitment and well-being is positive for those in the queer community. The current study examines whether an independent or interdependent self-construal explains the relationship between religious commitment and eudaimonic well-being (EWB) for lesbian and bisexual women. This empirical study used data from the Multi-Site University Study on Identity and Culture, a research collaboration amongst 30 colleges and universities in the United States. Findings suggest that although self-construal does not mediate the relationship between religious commitment and well-being, there are significant effects between religious commitment and EWB as well as an independent self-construal and EWB. These findings provide a deeper understanding of variables associated with greater well-being and are important for the advancement of research and practice with queer women.

An alternative approach to frequentist meta-analysis: A demonstration of Bayesian meta-analysis in adolescent development research.

INTRODUCTION: Past decades have seen a surge of applied and methodological research on meta-analysis. One methodological advancement that has gained significant traction is a Bayesian approach to meta-analysis. METHODS: We present a non-technical introduction to Bayesian meta-analysis. This introduction re-analyzes data from a meta-analysis concerning the impact of media literacy interventions on attitudes and intentions related to risky health behaviors using a Bayesian approach. One data relate media literacy interventions to media literacy skills, and another relates media literacy interventions to attitudes and behavioral intentions towards risky health behaviors. In these examples we focus on how to conduct unconditional models via graphical and quantitative results. Further, we demonstrate how to conduct subgroup analyses using risk behavior type (drinking, sexual, or smoking). RESULTS: We demonstrated how several meta-analytical quantities could be computed and interpreted in a Bayesian framework. This was done both graphically (plot of the marginal posterior distributions) and quantitatively (e.g., central tendency measures, highest posterior density intervals). Results also showed how analyzing effect sizes at the risk-behavior level could affect several interpretations. CONCLUSIONS: We emphasize that in no way are Bayesian methods "superior" to frequentist methods, nor that frequentist methods should be abandoned. Instead, the two approaches should be viewed as familial, each with advantages and disadvantages, but strive at a common purpose. We hope for increased use of Bayesian meta-analyses, and Bayesian methodology at large, in adolescence research. Last, all R code is provided for readers to use as a foundation for their own research.