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| Structural Equation Modeling | 7 |
Author
| Hau, Kit-Tai | 2 |
| Marsh, Herbert W. | 2 |
| Wen, Zhonglin | 2 |
| Bechger, Timo | 1 |
| Dolan, Conor | 1 |
| Foss, Tron | 1 |
| Howell, Roy D. | 1 |
| Millsap, Roger E. | 1 |
| Molenaar, Peter | 1 |
| Ogasawara, Haruhiko | 1 |
| Olsson, Ulf Henning | 1 |
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| Journal Articles | 7 |
| Reports - Descriptive | 7 |
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Peer reviewedMillsap, Roger E. – Structural Equation Modeling, 2001
Different sets of uniqueness constraints may lead to different fit results when applied to the same data in confirmatory factor analysis. Provides several examples of this phenomenon in simulated data and describes reasons for the variation in fit results. Discusses the choice of uniqueness constraints under these circumstances. (SLD)
Descriptors: Goodness of Fit, Simulation
Peer reviewedRaykov, Tenko – Structural Equation Modeling, 2001
Discusses a method, based on bootstrap methodology, for obtaining an approximate confidence interval for the difference in root mean square error of approximation of two structural equation models. Illustrates the method using a numerical example. (SLD)
Descriptors: Goodness of Fit, Structural Equation Models
Peer reviewedOgasawara, Haruhiko – Structural Equation Modeling, 2001
Derives approximations to the distributions of goodness-of-fit indexes in structural equation modeling with the assumption of multivariate normality and slight misspecification of models. Also derives an approximation to the asymptotic covariance matrix for the fit indexes by using the delta method and develops approximations to the densities of…
Descriptors: Goodness of Fit, Statistical Distributions, Structural Equation Models
Peer reviewedWen, Zhonglin; Marsh, Herbert W.; Hau, Kit-Tai – Structural Equation Modeling, 2002
Points out two concerns with recent research by F. Li and others (2000) and T. Duncan and others (1999) that extended the structural equation model of latent interactions developed by K. Joreskog and F. Yang (1996) to latent growth modeling. Used mathematical derivation and a comparison of alternative models fitted to simulated data to develop a…
Descriptors: Goodness of Fit, Interaction, Simulation, Structural Equation Models
Peer reviewedDolan, Conor; Bechger, Timo; Molenaar, Peter – Structural Equation Modeling, 1999
Considers models incorporating principal components from the perspectives of structural-equation modeling. These models include the following: (1) the principal-component analysis of patterned matrices; (2) multiple analysis of variance based on principal components; and (3) multigroup principal-components analysis. Discusses fitting these models…
Descriptors: Computer Software, Factor Analysis, Goodness of Fit, Matrices
Peer reviewedOlsson, Ulf Henning; Foss, Tron; Troye, Sigurd V.; Howell, Roy D. – Structural Equation Modeling, 2000
Used simulation to demonstrate how the choice of estimation method affects indexes of fit and parameter bias for different sample sizes when nested models vary in terms of specification error and the data demonstrate different levels of kurtosis. Discusses results for maximum likelihood (ML), generalized least squares (GLS), and weighted least…
Descriptors: Estimation (Mathematics), Goodness of Fit, Least Squares Statistics, Maximum Likelihood Statistics
Direct linkMarsh, Herbert W.; Hau, Kit-Tai; Wen, Zhonglin – Structural Equation Modeling, 2004
Goodness-of-fit (GOF) indexes provide "rules of thumb"?recommended cutoff values for assessing fit in structural equation modeling. Hu and Bentler (1999) proposed a more rigorous approach to evaluating decision rules based on GOF indexes and, on this basis, proposed new and more stringent cutoff values for many indexes. This article discusses…
Descriptors: Statistical Significance, Structural Equation Models, Evaluation Methods, Evaluation Research
