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Raykov, Tenko – Structural Equation Modeling, 2000
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)
Descriptors: Error of Measurement, Estimation (Mathematics), Sample Size, Statistical Bias
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Sivo, Stephen A.; Willson, Victor L. – Structural Equation Modeling, 2000
Studied whether moving average or autoregressive moving average models fit two longitudinal data sets previously thought to possess quasi-simplex structures better than the quasi-simplex, one-factor, or autoregressive models. Results of a Monte Carlo study show the importance of evaluating the fit, propriety, and parsimony of models before one…
Descriptors: Causal Models, Error of Measurement, Goodness of Fit, Longitudinal Studies
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Enders, Craig K.; Peugh, James L. – Structural Equation Modeling, 2004
Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no…
Descriptors: Inferences, Structural Equation Models, Factor Analysis, Error of Measurement
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Wang, Lin; And Others – Structural Equation Modeling, 1996
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)
Descriptors: Error of Measurement, Estimation (Mathematics), Goodness of Fit, Least Squares Statistics
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Bandalos, Deborah L. – Structural Equation Modeling, 1997
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…
Descriptors: Comparative Analysis, Error of Measurement, Estimation (Mathematics), Monte Carlo Methods
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Finch, John F.; And Others – Structural Equation Modeling, 1997
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)
Descriptors: Error of Measurement, Estimation (Mathematics), Maximum Likelihood Statistics, Monte Carlo Methods
Peer reviewed Peer reviewed
Anderson, Ronald D. – Structural Equation Modeling, 1996
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)
Descriptors: Error of Measurement, Estimation (Mathematics), Goodness of Fit, Maximum Likelihood Statistics
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McQuitty, Shaun – Structural Equation Modeling, 1997
LISREL 8 invokes a ridge option when maximum likelihood or generalized least squares are used to estimate a structural equation model with a nonpositive definite covariance or correlation matrix. Implications of the ridge option for model fit, parameter estimates, and standard errors are explored through two examples. (SLD)
Descriptors: Error of Measurement, Estimation (Mathematics), Goodness of Fit, Least Squares Statistics