Abstract
Although the maximum likelihood estimator based on normality theory is default in most available programs in structural equation modeling, the majority of data investigated in behavioral and social sciences violate the assumption of multivariate normality. This study evaluated six covariance structure analysis techniques (i.e., ML, GLS, ADF, SCALED, bootstrap-Mo and bootstrap-MA) under various conditions of nonnormality. Results clearly illustrated that the ML and GLS failed to provide a good control of Type I error rates in all conditions of nonnormality even with the sample size of 5000. The ADF was essentially unusable in small to intermediated sample sizes. The SCALED and two bootstrap methods provided promising advantages but they were confined by small sample sizes. Additionally, the minimum requirements of sample sizes and bootstrapped samples for bootstrapping procedures were identified. Finally, a few suggestions were provided in the hope of improving the current practice.
| Keywords: | ADF; SCALED; bootstrapping; covariance structure |
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