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Teacher's corner : evaluating informative hypotheses using the Bayes factor in structural equation models

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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.
Keywords
INEQUALITY-CONSTRAINED HYPOTHESES, LIKELIHOOD RATIO, Bain, bayes factor, informative hypotheses, structural equation modeling

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MLA
Van Lissa, Caspar J., et al. “Teacher’s Corner : Evaluating Informative Hypotheses Using the Bayes Factor in Structural Equation Models.” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 2021, doi:10.1080/10705511.2020.1745644.
APA
Van Lissa, C. J., Gu, X., Mulder, J., Rosseel, Y., Van Zundert, C., & Hoijtink, H. (2021). Teacher’s corner : evaluating informative hypotheses using the Bayes factor in structural equation models. STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL. https://doi.org/10.1080/10705511.2020.1745644
Chicago author-date
Van Lissa, Caspar J., Xin Gu, Joris Mulder, Yves Rosseel, Camiel Van Zundert, and Herbert Hoijtink. 2021. “Teacher’s Corner : Evaluating Informative Hypotheses Using the Bayes Factor in Structural Equation Models.” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL. https://doi.org/10.1080/10705511.2020.1745644.
Chicago author-date (all authors)
Van Lissa, Caspar J., Xin Gu, Joris Mulder, Yves Rosseel, Camiel Van Zundert, and Herbert Hoijtink. 2021. “Teacher’s Corner : Evaluating Informative Hypotheses Using the Bayes Factor in Structural Equation Models.” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL. doi:10.1080/10705511.2020.1745644.
Vancouver
1.
Van Lissa CJ, Gu X, Mulder J, Rosseel Y, Van Zundert C, Hoijtink H. Teacher’s corner : evaluating informative hypotheses using the Bayes factor in structural equation models. STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL. 2021;
IEEE
[1]
C. J. Van Lissa, X. Gu, J. Mulder, Y. Rosseel, C. Van Zundert, and H. Hoijtink, “Teacher’s corner : evaluating informative hypotheses using the Bayes factor in structural equation models,” STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 2021.
@article{8687601,
  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.}},
  author       = {{Van Lissa, Caspar J. and Gu, Xin and Mulder, Joris and Rosseel, Yves and Van Zundert, Camiel and Hoijtink, Herbert}},
  issn         = {{1070-5511}},
  journal      = {{STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL}},
  keywords     = {{INEQUALITY-CONSTRAINED HYPOTHESES,LIKELIHOOD RATIO,Bain,bayes factor,informative hypotheses,structural equation modeling}},
  language     = {{eng}},
  pages        = {{10}},
  title        = {{Teacher's corner : evaluating informative hypotheses using the Bayes factor in structural equation models}},
  url          = {{http://dx.doi.org/10.1080/10705511.2020.1745644}},
  year         = {{2021}},
}

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