Effects of Nonnormal Data on Parameter Estimates and Fit Indices for a Model With Latent and Manifest Variables: An Empirical Study

Research in covariance structure analysis suggests that nonnormal data will invalidate chi-square tests and produce erroneous standard errors. However, much remains unknown about the extent to and the conditions under which highly skewed and kurtotic data can affect the parameter estimates, standard errors, and fit indices. Using actual kurtotic and skewed data and varying sample sizes and estimation methods, we found that (a) normal theory maximum likelihood (ML) and generalized least squares estimators were fairly consistent and almost identical, (b) standard errors tended to underestimate the true variation of the estimators, but the problem was not very serious for large samples (n = 1,000) and conservative (99%) confidence intervals, and (c) the adjusted chi-square tests seemed to yield acceptable results with appropriate sample sizes.