Teacher’s corner: Evaluating informative hypotheses using the Bayes factor in structural equation models

Caspar van Lissa*, Xin Gu, Joris Mulder, Y. Rosseel, Camiel van Zundert, Herbert Hoijtink

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
13 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)292-301
JournalStructural Equation Modeling
Volume28
Issue number2
DOIs
Publication statusPublished - 2021

Keywords

  • Bain
  • INEQUALITY-CONSTRAINED HYPOTHESES
  • LIKELIHOOD RATIO
  • bayes factor
  • informative hypotheses
  • structural equation modeling

Fingerprint

Dive into the research topics of 'Teacher’s corner: Evaluating informative hypotheses using the Bayes factor in structural equation models'. Together they form a unique fingerprint.

Cite this