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Peer reviewedGerbing, David W.; Hamilton, Janet G. – Structural Equation Modeling, 1996
A Monte Carlo study evaluated the effectiveness of different factor analysis extraction and rotation methods for identifying the known population multiple-indicator measurement model. Results demonstrate that exploratory factor analysis can contribute to a useful heuristic strategy for model specification prior to cross-validation with…
Descriptors: Heuristics, Mathematical Models, Measurement Techniques, Monte Carlo Methods
Peer reviewedBrown, R. L. – Educational and Psychological Measurement, 1991
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)
Descriptors: Computer Simulation, Equations (Mathematics), Estimation (Mathematics), Mathematical Models
Download full textFan, Xitao; And Others – 1996
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…
Descriptors: Estimation (Mathematics), Goodness of Fit, Mathematical Models, Monte Carlo Methods
Peer reviewedFan, Xitao; Wang, Lin – Educational and Psychological Measurement, 1998
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)
Descriptors: Estimation (Mathematics), Goodness of Fit, Mathematical Models, Monte Carlo Methods
Fan, Xitao; And Others – 1997
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…
Descriptors: Estimation (Mathematics), Goodness of Fit, Mathematical Models, Monte Carlo Methods
Kaplan, David – 1993
The impact of the use of data arising from balanced incomplete block (BIB) spiralled designs on the chi-square goodness-of-fit test in factor analysis is considered. Data from BIB designs posses a unique pattern of missing data that can be characterized as missing completely at random (MCAR). Standard approaches to factor analyzing such data rest…
Descriptors: Chi Square, Computer Simulation, Correlation, Factor Analysis
Peer reviewedBrown, R. L. – Educational and Psychological Measurement, 1992
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…
Descriptors: Computer Simulation, Equations (Mathematics), Estimation (Mathematics), Least Squares Statistics
