The performance of ML, DWLS, and ULS estimation with robust corrections in structural equation models with ordinal variables.

Publisher: American Psychological Association Country of Publication: United States NLM ID: 9606928 Publication Model: Print Cited Medium: Internet ISSN: 1939-1463 (Electronic) Linking ISSN: 1082989X NLM ISO Abbreviation: Psychol Methods Subsets: MEDLINE

Original Publication: Washington, DC : American Psychological Association, c1996-

Least-Squares Analysis* ; Models, Theoretical* ; Monte Carlo Method*; Humans ; Likelihood Functions ; Sample Size

Three estimation methods with robust corrections-maximum likelihood (ML) using the sample covariance matrix, unweighted least squares (ULS) using a polychoric correlation matrix, and diagonally weighted least squares (DWLS) using a polychoric correlation matrix-have been proposed in the literature, and are considered to be superior to normal theory-based maximum likelihood when observed variables in latent variable models are ordinal. A Monte Carlo simulation study was carried out to compare the performance of ML, DWLS, and ULS in estimating model parameters, and their robust corrections to standard errors, and chi-square statistics in a structural equation model with ordinal observed variables. Eighty-four conditions, characterized by different ordinal observed distribution shapes, numbers of response categories, and sample sizes were investigated. Results reveal that (a) DWLS and ULS yield more accurate factor loading estimates than ML across all conditions; (b) DWLS and ULS produce more accurate interfactor correlation estimates than ML in almost every condition; (c) structural coefficient estimates from DWLS and ULS outperform ML estimates in nearly all asymmetric data conditions; (d) robust standard errors of parameter estimates obtained with robust ML are more accurate than those produced by DWLS and ULS across most conditions; and (e) regarding robust chi-square statistics, robust ML is inferior to DWLS and ULS in controlling for Type I error in almost every condition, unless a large sample is used (N = 1,000). Finally, implications of the findings are discussed, as are the limitations of this study as well as potential directions for future research. (PsycINFO Database Record
((c) 2016 APA, all rights reserved).)

Date Created: 20160830 Date Completed: 20180417 Latest Revision: 20181202

20231215

10.1037/met0000093

27571021