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

Matti T J Heino,Matti Vuorre,Nelli Hankonen

doi:10.1080/21642850.2018.1428102

Abstract

ABSTRACTIntroductionEvaluating 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 the exclusive use of frequentist reasoning are now widespread in psychology and allied fields. We suggest adding Bayesian statistical methods to the researcher’s toolbox of statistical methods.ObjectivesWe first present the basic principles of the 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.Conclusion: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.

Highlights

Evaluating effects of behavior change interventions is a central interest in health psychology and behavioral medicine
It may come as a surprise that many statisticians consider these debates outdated: ‘We have all, or most, moved past these old debates, yet our textbook explanations have not caught up with the eclecticism of statistical practice’ (Kass, 2011)
Bayesian methods allow for the researcher to incorporate prior information regarding the research topic in evaluating the data, which allows for improvements in out-of-sample prediction

Summary

Introduction

After being conceived by the clergyman Thomas Bayes and astronomer-mathematician Pierre-Simon Laplace in the 1700s, spent two centuries in relative obscurity before surfacing again in the mid-1900s, with the rise of modern computing (McGrayne, 2011). Bayesian methods allow for the researcher to incorporate prior information regarding the research topic in evaluating the data, which allows for improvements in out-of-sample prediction In this tutorial, we present an introductory-level overview on the Bayesian approach to statistical inference and a practical tutorial on applying Bayesian methods to analyzing effects of behavior change interventions that use an experimental design. Suppose we observed an increase of autonomous motivation score by a whopping 2.1 on average in a group of 100 people The likelihood of this data, as a result of our chosen likelihood model, is represented by a normal distribution with a mean of 2.1 and a standard deviation of √SD (see Dienes (2008), p.

Procedure

Summary of practical tutorial

Ethics Statement

Findings

Conclusions and recommendations