Examines the use of sample weights in latent variable models in the case where a simple random sample is drawn from a population containing a mixture of strata through a bootstrap simulation study. Results show that ignoring weights can lead to serious bias in latent variable model parameters and reveal the advantages of using sample weights. (SLD)

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.…

Examined the effects of ignoring multilevel data structures in nonhierarchical covariance modeling using a Monte Carlo simulation. Results suggest that when the magnitudes of intraclass correlations are less than 0.05 and the group size is small, the consequences of ignoring the data dependence within the multilevel data structures seem to be…

Simulation results show that the power to detect small mean differences when fitting a model with free residual variances across groups decreases as the difference in R squared increases. This decrease is more pronounced in the presence of correlated errors and if group sample sizes differ. (SLD)

Studied the use of different weighting techniques in structural equation modeling and found, through simulation, that the use of an effective sample size weight provides unbiased estimates of key parameters and their sampling variances. Also discusses use of a popular normalization technique of scaling weights. (SLD)

Assessed the robustness of an estimation method for multilevel and path analysis with hierarchical data proposed by B. Muthen (1989) with unequal groups and small sample sizes and in the presence of a low or high intraclass correlation. Simulation results show the effects of varying these conditions on the within-group and between-groups part of…

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)