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 -