TY - JOUR

T1 - Teacher’s corner

T2 - Evaluating informative hypotheses using the Bayes factor in structural equation models

AU - van Lissa, Caspar

AU - Gu, Xin

AU - Mulder, Joris

AU - Rosseel, Y.

AU - van Zundert, Camiel

AU - Hoijtink, Herbert

N1 - Funding Information:
The first author is supported by an NWO Veni grant (NWO grant number VI.Veni.191G.090). The third author is supported by an NWO Vidi Grant (NWO grant number 452-17-006). The last author is supported by a fellowship from the Netherlands Institute for Advanced Studies in the Humanities and Social Sciences, and the Consortium on Individual Development (CID) which is funded through the Gravitation program of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.001.003).

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Bain

KW - INEQUALITY-CONSTRAINED HYPOTHESES

KW - LIKELIHOOD RATIO

KW - bayes factor

KW - informative hypotheses

KW - structural equation modeling

UR - http://www.scopus.com/inward/record.url?scp=85086519552&partnerID=8YFLogxK

UR - https://app-eu.readspeaker.com/cgi-bin/rsent?customerid=10118&lang=en_us&readclass=rs_readArea&url=https%3A%2F%2Fwww.tandfonline.com%2Fdoi%2Ffull%2F10.1080%2F10705511.2020.1745644&dict=math&rule=math&xslrule=math

U2 - 10.1080/10705511.2020.1745644

DO - 10.1080/10705511.2020.1745644

M3 - Article

VL - 28

SP - 292

EP - 301

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

IS - 2

ER -