Non-normality of Data in Structural Equation Models

Using census block groups data on socio-demographics, land use, and travel behavior, we test the cutoffs suggested in the literature for trustworthy estimates and hypothesis testing statistics, and evaluate the efficacy of deleting observations as an approach to improving multivariate normality, in structural equation modeling. The results show that the measures of univariate and multivariate non-normalities will fall into the acceptable ranges for trustworthy maximum likelihood estimation after a few true outliers are deleted. We argue that pursuing a multivariate normal distribution by deleting observations should be balanced against loss of model power in the interpretation of the results.