Bayesian evaluation of behavior change interventions: A brief introduction and a practical example

Abstract

Evaluating effects of behavior change interventions is a central interest in health psychology and behavioral medicine. Researchers in these fields routinely use frequentist statistical methods to evaluate the extent to which these interventions impact behavior and the hypothesized mediating processes in the population. However, calls to move beyond exclusive use of frequentist reasoning are now widespread in psychology and allied fields. We suggest adding Bayesian statistical methods to the researcher’s toolbox. We first present the basic principles of Bayesian approach to statistics and why they are useful for researchers in health psychology. We then provide a practical example on how to evaluate intervention effects using Bayesian methods, with a focus on Bayesian hierarchical modeling. We provide the necessary materials for introductory level readers to follow the tutorial. We also highlight differences between frequentist and Bayesian approaches throughout the tutorial. Bayesian analytical methods are now available to researchers through easy-to-use software packages, and we recommend using them to evaluate the effectiveness of interventions for their conceptual and practical benefits.

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