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 language | English |
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Pages (from-to) | 292-301 |
Journal | Structural Equation Modeling |
Volume | 28 |
Issue number | 2 |
Early online date | 29 May 2020 |
DOIs | |
Publication status | Published - 2021 |
Bibliographical note
Publisher Copyright:© 2020, © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Keywords
- Bain
- bayes factor
- informative hypotheses
- structural equation modeling