Teacher's Corner: Evaluating Informative Hypotheses Using the Bayes Factor in Structural Equation Models

• Published in: Structural equation modeling Vol. 28; no. 2; pp. 292 - 301
• Main Authors: Van Lissa, Caspar J., Gu, Xin, Mulder, Joris, Rosseel, Yves, Van Zundert, Camiel, Hoijtink, Herbert
• Format: Journal Article
• Language: English
• Published: Hove Routledge 04.03.2021; Psychology Press
• Subjects: Algorithms; Bain; bayes factor; Factor Analysis; Hypotheses; Hypothesis testing; informative hypotheses; Structural equation modeling; Structural Equation Models
• ISSN: 1070-5511; 1532-8007; 1532-8007
• DOI: 10.1080/10705511.2020.1745644

This Teacher's Corner paper introduces Bayesian evaluation of informative hypotheses for structural equation models, using the free open-source R packages bain, for Bayesian informative hypothesis testing, and lavaan, a widely used SEM package. The introduction provides a brief non-technical explanation of informative hypotheses, the statistical underpinnings of Bayesian hypothesis evaluation, and the bain algorithm. Three tutorial examples demonstrate informative hypothesis evaluation in the context of common types of structural equation models: 1) confirmatory factor analysis, 2) latent variable regression, and 3) multiple group analysis. We discuss hypothesis formulation, the interpretation of Bayes factors and posterior model probabilities, and sensitivity analysis.