An alternative approach to frequentist meta-analysis: A demonstration of Bayesian meta-analysis in adolescent development research. (July 2020)

• Record Type: Journal Article
• Authors: Thompson, Christopher G.; Semma, Brandie
• Is Part Of: Journal of adolescence. Volume 82(2020)
• Page Start: 86
• Page End: 102
• Publication Date: 2020-07
• Subjects: Adolescent psychiatry -- Periodicals; Adolescent psychology -- Periodicals; Adolescence -- Periodicals; Electronic journals; 305.23505
• DOI: 10.1016/j.adolescence.2020.05.001
• Languages: English
• ISSNs: 0140-1971

Abstract

Abstract: 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.