Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes.
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| Title: | Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes. |
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| Authors: | Leite, WalterL. (AUTHOR), Stapleton, LauraM. (AUTHOR) |
| Source: | Journal of Experimental Education. 2011, Vol. 79 Issue 4, p361-381. 21p. 2 Charts, 7 Graphs. |
| Subjects: | Linear statistical models, Monte Carlo method, Nonlinear theories, Longitudinal method, Analysis of covariance |
| Abstract: | In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification when a linear model was fit to scores presenting nonlinear growth trajectories, in terms of being sensitive to severity of misspecification, and providing stable results with different types of nonlinearity and sample sizes. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification when a linear model was fit to scores presenting nonlinear growth trajectories, in terms of being sensitive to severity of misspecification, and providing stable results with different types of nonlinearity and sample sizes. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00220973 |
| DOI: | 10.1080/00220973.2010.509369 |
