Bayesian Hierarchical Modeling: An Introduction and Reassessment

Abstract

With the recent development of easy-to-use tools for Bayesian analysis, psychologists have started to embrace Bayesian hierarchical modeling. Bayesian hierarchical models provide an intuitive account of inter- and intraindividual variability and are particularly suited for the evaluation of repeated-measures designs. Here, we provide guidance for model specificaton and interpretation in Bayesian hierarchical modeling and describe common pitfalls that can arise in the process of model fitting and evaluation. Our introduction gives particular emphasis to prior specification and prior sensitivity, as well as to the calculation of Bayes factors for model comparisons. We illustrate the use of state-of-the-art software programs Stan and brms. The result is an overview over best practices in Bayesian hierarchical modeling that, as we hope, will help psychologists in making the best use of Bayesian hierarchical modeling.

Authors 3

1. Myrthe Veenman corresponding Aachen

   Leiden University

   Affiliation as printed

   Leiden University

2. Angelika Marlene Stefan

   Universität der Bundeswehr München

   Affiliation as printed

   Universität der Bundeswehr München

3. Julia M. Haaf

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam