Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares

• Published in: Behavior research methods Vol. 48; no. 3; pp. 936 - 949
• Main Author: Li, Cheng-Hsien
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2016; Springer Nature B.V
• Subjects: Behavioral Science and Psychology; Chi-Square Distribution; Cognitive Psychology; Correlation analysis; Data Interpretation, Statistical; Discriminant analysis; Factor Analysis, Statistical; Humans; Least-Squares Analysis; Likelihood Functions; Maximum likelihood method; Models, Statistical; Monte Carlo Method; Monte Carlo simulation; Normal distribution; Parameter estimation; Psychology; Sample Size; Confirmatory factor analysis; Robust estimation; Monte Carlo Simulation; Ordinal data
• ISSN: 1554-3528; 1554-3528
• DOI: 10.3758/s13428-015-0619-7

In confirmatory factor analysis (CFA), the use of maximum likelihood (ML) assumes that the observed indicators follow a continuous and multivariate normal distribution, which is not appropriate for ordinal observed variables. Robust ML (MLR) has been introduced into CFA models when this normality assumption is slightly or moderately violated. Diagonally weighted least squares (WLSMV), on the other hand, is specifically designed for ordinal data. Although WLSMV makes no distributional assumptions about the observed variables, a normal latent distribution underlying each observed categorical variable is instead assumed. A Monte Carlo simulation was carried out to compare the effects of different configurations of latent response distributions, numbers of categories, and sample sizes on model parameter estimates, standard errors, and chi-square test statistics in a correlated two-factor model. The results showed that WLSMV was less biased and more accurate than MLR in estimating the factor loadings across nearly every condition. However, WLSMV yielded moderate overestimation of the interfactor correlations when the sample size was small or/and when the latent distributions were moderately nonnormal. With respect to standard error estimates of the factor loadings and the interfactor correlations, MLR outperformed WLSMV when the latent distributions were nonnormal with a small sample size of N = 200. Finally, the proposed model tended to be over-rejected by chi-square test statistics under both MLR and WLSMV in the condition of small sample size N = 200.