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ERIC Number: EJ1004401
Record Type: Journal
Publication Date: 2012-Sep
Pages: 20
Abstractor: As Provided
Reference Count: 71
ISSN: ISSN-1082-989X
When Can Categorical Variables Be Treated as Continuous? A Comparison of Robust Continuous and Categorical SEM Estimation Methods under Suboptimal Conditions
Rhemtulla, Mijke; Brosseau-Liard, Patricia E.; Savalei, Victoria
Psychological Methods, v17 n3 p354-373 Sep 2012
A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category thresholds. Results revealed that factor loadings and robust standard errors were generally most accurately estimated using cat-LS, especially with fewer than 5 categories; however, factor correlations and model fit were assessed equally well with ML. Cat-LS was found to be more sensitive to sample size and to violations of the assumption of normality of the underlying continuous variables. Normal theory ML was found to be more sensitive to asymmetric category thresholds and was especially biased when estimating large factor loadings. Accordingly, we recommend cat-LS for data sets containing variables with fewer than 5 categories and ML when there are 5 or more categories, sample size is small, and category thresholds are approximately symmetric. With 6-7 categories, results were similar across methods for many conditions; in these cases, either method is acceptable. (Contains 9 figures, 2 tables, and 13 footnotes.)
American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Adult Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A