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] |
| Copyright of Journal of Experimental Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 64374712 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Leite%2C+WalterL%2E%22">Leite, WalterL.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Stapleton%2C+LauraM%2E%22">Stapleton, LauraM.</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Experimental+Education%22">Journal of Experimental Education</searchLink>. 2011, Vol. 79 Issue 4, p361-381. 21p. 2 Charts, 7 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Linear+statistical+models%22">Linear statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+theories%22">Nonlinear theories</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+covariance%22">Analysis of covariance</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Experimental Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=64374712 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00220973.2010.509369 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 361 Subjects: – SubjectFull: Linear statistical models Type: general – SubjectFull: Monte Carlo method Type: general – SubjectFull: Nonlinear theories Type: general – SubjectFull: Longitudinal method Type: general – SubjectFull: Analysis of covariance Type: general Titles: – TitleFull: Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Leite, WalterL. – PersonEntity: Name: NameFull: Stapleton, LauraM. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 00220973 Numbering: – Type: volume Value: 79 – Type: issue Value: 4 Titles: – TitleFull: Journal of Experimental Education Type: main |
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