Small-Variance Priors in Bayesian Factor Analysis with Ordinal Data

Autor/inn/en	Liang, Xinya; Cao, Chunhua
Titel	Small-Variance Priors in Bayesian Factor Analysis with Ordinal Data
Quelle	In: Journal of Experimental Education, 91 (2023) 4, S.739-764 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Liang, Xinya) ORCID (Cao, Chunhua)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2022.2100731
Schlagwörter	Factor Analysis; Bayesian Statistics; Structural Equation Models; Simulation; Classification; Decision Making; Item Analysis; Evaluation Methods + Suchen Sie Ihr Suchwort? Faktorenanalyse; Simulation program; Simulationsprogramm; Classification system; Klassifikation; Klassifikationssystem; Decision-making; Entscheidungsfindung; Itemanalyse
Abstract	To evaluate multidimensional factor structure, a popular method that combines features of confirmatory and exploratory factor analysis is Bayesian structural equation modeling with small-variance normal priors (BSEM-N). This simulation study evaluated BSEM-N as a variable selection and parameter estimation tool in factor analysis with sparse cross-loading structures, focusing on ordered categorical data. A sensitivity analysis was conducted by assigning eight choices of small-variance priors on all potential cross-loadings. Results indicated that variable selection was performed well in a sparse loading structure in which the number of essential cross-loadings was small and the magnitudes were relatively large. Characteristics of ordinal items did not seem to have a sizable impact on parameter estimation. If the number of cross-loading estimates were small and centered around zero, BSEM-N may serve more efficiently as a tool for parameter estimation. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01