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Gold, Michael Steven; Bentler, Peter M. – Structural Equation Modeling, 2000
Describes a Monte Carlo investigation of four methods for treating incomplete data: (1) resemblance based hot-deck imputation (RBHDI); (2) iterated stochastic regression imputation; (3) structured model expectation maximization; and (4) saturated model expectation maximization. Results favored the expectation maximization methods. (SLD)
Descriptors: Monte Carlo Methods, Regression (Statistics)
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Hancock, Gregory R.; Lawrence, Frank R.; Nevitt, Jonathan – Structural Equation Modeling, 2000
Studied Type I error rates and relative power of structural means, multiple-indicator, multiple-cause, and multivariate analysis of variance approaches for testing construct mean differences within a one-factor, two-group design. Used Monte Carlo methods to investigate Type I error rates and a population analysis approach to study the power of…
Descriptors: Analysis of Variance, Monte Carlo Methods
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Julian, Marc W. – Structural Equation Modeling, 2001
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…
Descriptors: Correlation, Monte Carlo Methods, Sample Size, Simulation
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Coenders, Germa; Saris, Willem E.; Satorra, Albert – Structural Equation Modeling, 1997
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…
Descriptors: Estimation (Mathematics), Monte Carlo Methods, Structural Equation Models
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Bacon, Donald R. – Structural Equation Modeling, 2001
Evaluated the performance of several alternative cluster analytic approaches to initial model specification using population parameter analyses and a Monte Carlo simulation. Of the six cluster approaches evaluated, the one using the correlations of item correlations as a proximity metric and average linking as a clustering algorithm performed the…
Descriptors: Algorithms, Cluster Analysis, Correlation, Mathematical Models
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Enders, Craig K.; Bandalos, Deborah L. – Structural Equation Modeling, 2001
Used Monte Carlo simulation to examine the performance of four missing data methods in structural equation models: (1)full information maximum likelihood (FIML); (2) listwise deletion; (3) pairwise deletion; and (4) similar response pattern imputation. Results show that FIML estimation is superior across all conditions of the design. (SLD)
Descriptors: Maximum Likelihood Statistics, Monte Carlo Methods, Simulation, Structural Equation Models
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Olmos, Antonio; Hutchinson, Susan R. – Structural Equation Modeling, 1998
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)
Descriptors: Estimation (Mathematics), Goodness of Fit, Monte Carlo Methods, Sample Size
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Song, Xin-Yuan; Lee, Sik-Yum; Zhu, Hong-Tu – Structural Equation Modeling, 2001
Studied the maximum likelihood estimation of unknown parameters in a general LISREL-type model with mixed polytomous and continuous data through Monte Carlo simulation. Proposes a model selection procedure for obtaining good models for the underlying substantive theory and discusses the effectiveness of the proposed model. (SLD)
Descriptors: Maximum Likelihood Statistics, Monte Carlo Methods, Selection, Simulation
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Gerbing, 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
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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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Jackson, Dennis L. – Structural Equation Modeling, 2001
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…
Descriptors: Estimation (Mathematics), Maximum Likelihood Statistics, Monte Carlo Methods, Reliability
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Fouladi, Rachel T. – Structural Equation Modeling, 2000
Provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and details Monte Carlo simulation results on Type I and Type II error control. Demonstrates through the simulation that robustness and nonrobustness of structure analysis techniques vary as a…
Descriptors: Analysis of Covariance, Correlation, Monte Carlo Methods, Multivariate Analysis
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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