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K. Bollen, J. Long (1993)
Testing Structural Equation Models
R. Brown (1989)
Using Covariance Modeling for Estimating Reliability on Scales with Ordered Polytomous VariablesEducational and Psychological Measurement, 49
P. Curran, S. West, J. Finch (1996)
The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis.Psychological Methods, 1
B. Muthén, D. Kaplan, M. Hollis (1987)
On structural equation modeling with data that are not missing completely at randomPsychometrika, 52
F. Chen, K.A. Bollen, P. Paxton, P.J. Curran, J.B. Kirby (2001)
Improper solutions in structural equation models: causes, consequences, and strategiesSociol. Methods Res., 29
C. Distefano (2002)
The Impact of Categorization With Confirmatory Factor AnalysisStructural Equation Modeling: A Multidisciplinary Journal, 9
P. Bentler, K. Yuan (1999)
Structural Equation Modeling with Small Samples: Test Statistics.Multivariate behavioral research, 34 2
K. Yuan, P. Bentler (1998)
Normal theory based test statistics in structural equation modelling.The British journal of mathematical and statistical psychology, 51 ( Pt 2)
A. Satorra, P. Bentler (1994)
Corrections to test statistics and standard errors in covariance structure analysis.
G. Coenders, A. Satorra, W. Saris (1997)
Alternative approaches to structural modeling of ordinal data: A Monte Carlo studyStructural Equation Modeling, 4
P.M. Bentler, E.J.C. Wu (2004)
EQS 6 for Windows User’s Guide
J. Hoogland, A. Boomsma (1998)
Robustness Studies in Covariance Structure ModelingSociological Methods & Research, 26
U. Olsson (1979)
Maximum likelihood estimation of the polychoric correlation coefficientPsychometrika, 44
Charles Brody, Tammy Greer, Alexander Eye, C. Clogg (1995)
Latent Variables Analysis: Applications for Developmental Research.Journal of the American Statistical Association, 90
P. Bentler (1989)
EQS : structural equations program manual
J. Nevitt, G. Hancock (2004)
Evaluating Small Sample Approaches for Model Test Statistics in Structural Equation ModelingMultivariate Behavioral Research, 39
K. Bollen (1989)
Structural Equations with Latent Variables
B. Muthén, SH Du, D. Špišić, B. Muthén, Shc Toit (1997)
Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes
P. Curran, K. Bollen, Pamela Paxton, James Kirby, Feinian Chen (2002)
The Noncentral Chi-square Distribution in Misspecified Structural Equation Models: Finite Sample Results from a Monte Carlo SimulationMultivariate Behavioral Research, 37
Feinian Chen, K. Bollen, Pamela Paxton, P. Curran, James Kirby (2001)
Improper Solutions in Structural Equation ModelsSociological Methods & Research, 29
J.J. Hoogland, A. Boomsma (1998)
Robustness studies in covariance structural modeling: an overview and a meta-analysisSociol. Method Res., 26
T. Micceri (1989)
The unicorn, the normal curve, and other improbable creatures.Psychological Bulletin, 105
D. Flora, P. Curran (2004)
An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data.Psychological methods, 9 4
J.V. Bradley (1978)
Robustness?Br. J. Math. Statis. Psychol., 31
Sik-Yum Lee, W. Poon, P. Bentler (1995)
A two-stage estimation of structural equation models with continuous and polytomous variables.The British journal of mathematical and statistical psychology, 48 ( Pt 2)
B. Muthén (1984)
A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicatorsPsychometrika, 49
This study examined the performance of two alternative estimation approaches in structural equation modeling for ordinal data under different levels of model misspecification, score skewness, sample size, and model size. Both approaches involve analyzing a polychoric correlation matrix as well as adjusting standard error estimates and model chi-squared, but one estimates model parameters with maximum likelihood and the other with robust weighted least-squared. Relative bias in parameter estimates and standard error estimates, Type I error rate, and empirical power of the model test, where appropriate, were evaluated through Monte Carlo simulations. These alternative approaches generally provided unbiased parameter estimates when the model was correctly specified. They also provided unbiased standard error estimates and adequate Type I error control in general unless sample size was small and the measured variables were moderately skewed. Differences between the methods in convergence problems and the evaluation criteria, especially under small sample and skewed variable conditions, were discussed.
Quality & Quantity – Springer Journals
Published: Sep 22, 2007
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