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ERIC Number: EJ1201543
Record Type: Journal
Publication Date: 2019-Feb
Pages: 21
Abstractor: As Provided
ISSN: ISSN-0013-1644
Categorical Omega with Small Sample Sizes via Bayesian Estimation: An Alternative to Frequentist Estimators
Yang, Yanyun; Xia, Yan
Educational and Psychological Measurement, v79 n1 p19-39 Feb 2019
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A