Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

Autor/inn/en	Leite, Walter L.; Stapleton, Laura M.
Titel	Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes
Quelle	In: Journal of Experimental Education, 79 (2011) 4, S.361-381 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2010.509369
Schlagwörter	Structural Equation Models; Simulation; Geometric Concepts; Sample Size; Longitudinal Studies; Comparative Analysis; Evaluation Methods + Suchen Sie Ihr Suchwort? Simulation program; Simulationsprogramm; Elementare Geometrie; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung
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. (Contains 2 tables and 7 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10