Teacher's Corner: Evaluating Informative Hypotheses Using the Bayes Factor in Structural Equation Models. Issue 2 (4th March 2021)

• Record Type: Journal Article
• Authors: Van Lissa, Caspar J.; Gu, Xin; Mulder, Joris; Rosseel, Yves; Van Zundert, Camiel; Hoijtink, Herbert
• Is Part Of: Structural equation modeling. Volume 28:Issue 2(2021)
• Page Start: 292
• Page End: 301
• Publication Date: 2021-03-04
• Subjects: Bain -- bayes factor -- informative hypotheses -- structural equation modeling; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2020.1745644
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: 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.