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A large-scale simulation design was used to study the sensitivity of maximum likelihood (ML) factor analysis to violations of measurement scale and distributional assumptions in the input data. Product-moment, polychoric. Spearman's rho, and Kendall's tau- b correlations computed from ordinal data were used to estimate a single-factor model. The resulting ML estimates were compared on the bases of convergence rates and improper solutions, accuracy of the loading estimates, fit statistics, and estimated standard errors. The LISREL maximum likelihood solution algorithm was used to estimate model parameters. The polychoric correlation procedure was found to provide the most accurate estimates of pairwise correlations and factor loadings but performed worst on all goodness-of-fit criteria. LISREL overestimated all standard errors, thus not reflecting the effects of standardization as previously assumed. When the data were categorized, the polychoric correlations led to the best estimates of the standard errors.
Babakus et al. (Fri,) studied this question.