Examined 20 goodness-of-fit indexes based on the minimum fit function using a simulation under the 2-group situation. Results support the use of the delta comparative fit index, delta Gamma hat, and delta McDonald's Noncentrality Index to evaluation measurement invariance. These three approaches are independent of model complexity and sample size.…

The relation among fit indexes, power, and sample size in structural equation modeling is examined. The noncentrality parameter is required to compute power. The 2 existing methods of computing power have estimated the noncentrality parameter by specifying an alternative hypothesis or alternative fit. These methods cannot be implemented easily and…

Sample covariance matrices constructed with pairwise deletion for randomly missing data were used in a simulation with three sample sizes and five levels of missing data (up to 50%). Parameter estimates were unbiased, parameter variability was largely explicable, and no sample covariance matrices were nonpositive definite except for 50% missing…

The behavior of eight measures of fit used to evaluate confirmatory factor analysis models was studied through Monte Carlo simulation to determine the extent to which sample size, model size, estimation procedure, and level of nonnormality affect fit when analyzing polytomous data. Implications of results for evaluating fit are discussed. (SLD)

A Monte Carlo simulation study investigated the effects on 10 structural equation modeling fit indexes of sample size, estimation method, and model specification. Some fit indexes did not appear to be comparable, and it was apparent that estimation method strongly influenced almost all fit indexes examined, especially for misspecified models. (SLD)

Used simulation to demonstrate how the choice of estimation method affects indexes of fit and parameter bias for different sample sizes when nested models vary in terms of specification error and the data demonstrate different levels of kurtosis. Discusses results for maximum likelihood (ML), generalized least squares (GLS), and weighted least…

Actual kurtotic and skewed data and varied sample sizes and estimation methods demonstrated that normal theory maximum likelihood and generalized least square estimators were fairly consistent and almost identical. Standard errors tended to underestimate the estimator's true variation but the problem was not serious for large samples. (SLD)

Goodness of fit indexes developed by R. P. McDonald (1989) and Satorra-Bentler scale correction methods (A. Satorra and P. M. Bentler, 1988) were studied. The Satorra-Bentler index is shown to have the least error under each distributional misspecification level when the model has correct structural specification. (SLD)