Shows that the conventional noncentrality parameter estimator of covariance structure models, currently implemented in popular structural modeling programs, possesses asymptotically potentially large bias, variance, and mean squared error (MSE). Presents a formal expression for its large-sample bias and quantifies large-sample bias and MSE. (SLD)

The primary purpose of this study was to examine relative performance of 2 power estimation methods in structural equation modeling. Sample size, alpha level, type of manifest variable, type of specification errors, and size of correlation between constructs were manipulated. Type 1 error rate of the model chi-square test, empirical critical…

A Monte Carlo approach was used to examine bias in the estimation of indirect effects and their associated standard errors. Results illustrate the adverse effects of nonnormality on the accuracy of significance tests in latent variable models estimated using normal theory maximum likelihood statistics. (SLD)