Categorical Omega with Small Sample Sizes via Bayesian Estimation: An Alternative to Frequentist Estimators

Autor/inn/en	Yang, Yanyun; Xia, Yan
Titel	Categorical Omega with Small Sample Sizes via Bayesian Estimation: An Alternative to Frequentist Estimators
Quelle	In: Educational and Psychological Measurement, 79 (2019) 1, S.19-39 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164417752008
Schlagwörter	Scores; Sample Size; Bayesian Statistics; Item Analysis; Classification; Least Squares Statistics; Evaluation Methods; Factor Analysis; Structural Equation Models; Factor Structure + Suchen Sie Ihr Suchwort? Itemanalyse; Classification system; Klassifikation; Klassifikationssystem; Faktorenanalyse; Faktorenstruktur
Abstract	When item scores are ordered categorical, categorical omega can be computed based on the parameter estimates from a factor analysis model using frequentist estimators such as diagonally weighted least squares. When the sample size is relatively small and thresholds are different across items, using diagonally weighted least squares can yield a substantially biased estimate of categorical omega. In this study, we applied Bayesian estimation methods for computing categorical omega. The simulation study investigated the performance of categorical omega under a variety of conditions through manipulating the scale length, number of response categories, distributions of the categorical variable, heterogeneities of thresholds across items, and prior distributions for model parameters. The Bayes estimator appears to be a promising method for estimating categorical omega. M"plus" and SAS codes for computing categorical omega were provided. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01