A Monte Carlo study is reported that shows the comparative performance of alternative approaches under deviations from their respective assumptions in the case of structural equation models with latent variables with attention restricted to point estimates of model parameters. The conditional polychoric correlations method is shown most robust…

Proposes the use of nonnested tests for the two stage least squares (2SLS) estimator of latent variable models to discriminate between scales. Compares the finite sample performance of these tests to structural equation modeling information-based criteria. Presents practical recommendations based on the Monte Carlo analysis. (SLD)

Monte Carlo simulation compared chi-square statistics, parameter estimates, and root mean square error of approximation values using normal and elliptical estimation methods. Three research conditions were imposed on the simulated data: sample size, population contamination percent, and kurtosis. A Bentler-Weeks structural model established the…

The SEPATH structural equation modeling (SEM) software is a new module in the latest release of STATISTICA (version 5.0) for Windows 3.1 and Windows 95. SEPATH is a program that provides a comprehensive set of functions for the SEM modeling. The interface and the Monte Carlo capability are strong features. (SLD)

The effect that collapsing ordered polytomous variable scales has on structural equation measurement model parameter estimates was examined. Four parameter estimation procedures were investigated in a Monte Carlo study. Collapsing categories in ordered polytomous variables had little effect when latent projection procedures were used. (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)

Investigated the assumption that determining an adequate sample size in structural equation modeling can be aided by considering the number of parameters to be estimated. Findings from maximum likelihood confirmatory factor analysis support previous research on the effect of sample size, measured variable reliability, and the number of measured…

A Monte Carlo simulation study was conducted to investigate the effects of sample size, estimation method, and model specification on structural equation modeling (SEM) fit indices. Based on a balanced 3x2x5 design, a total of 6,000 samples were generated from a prespecified population covariance matrix, and eight popular SEM fit indices were…

This study empirically investigated bootstrap bias estimation in the area of structural equation modeling (SEM). Three correctly specified SEM models were used under four different sample size conditions. Monte Carlo experiments were carried out to generate the criteria against which bootstrap bias estimation should be judged. For SEM fit indices,…

Studied the small sample behavior of several test statistics based on the maximum-likelihood estimator but designed to perform better with nonnormal data. Monte Carlo results indicate the satisfactory performance of the "F" statistic recently proposed by K. Yuan and P. Bentler (1997). (SLD)

In this Monte Carlo study, the effects of four factors on structural equation modeling (SEM) fit indices and parameter estimates were investigated. The 14,400 samples generated were fitted to 3 SEM models with different degrees of model misspecification. Effects of data nonnormality, estimation method, and sample size are noted. (SLD)

Monte Carlo methods were used to study the accuracy and utility of estimators of overall error and error due to approximation in structural equation modeling. Effects of sample size, indicator reliabilities, and degree of misspecification were examined. The rescaled noncentrality parameter also was examined. Choosing among competing models is…

A Monte Carlo study was conducted to assess the effects of some potential confounding factors on structural equation modeling (SEM) fit indices and parameter estimates for both true and misspecified models. The factors investigated were data nonnormality, SEM estimation method, and sample size. Based on the fully crossed and balanced 3x3x4x2…

Though common structural equation modeling (SEM) methods are predicated upon the assumption of multivariate normality, applied researchers often find themselves with data clearly violating this assumption and without sufficient sample size to use distribution-free estimation methods. Fortunately, promising alternatives are being integrated into…

A Monte Carlo study explores the robustness assumption in structural equation modeling of using a full information normal theory generalized least-squares estimation procedure on Type I censored data. The efficacy of the following proposed alternate estimation procedures is assessed: asymptotically distribution free estimator and a latent…