Specification Search for Identifying the Correct Mean Trajectory in Polynomial Latent Growth Models

Autor/inn/en	Kim, Minjung; Kwok, Oi-Man; Yoon, Myeongsun; Willson, Victor; Lai, Mark H. C.
Titel	Specification Search for Identifying the Correct Mean Trajectory in Polynomial Latent Growth Models
Quelle	In: Journal of Experimental Education, 84 (2016) 2, S.307-329 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0973
DOI	10.1080/00220973.2014.984831
Schlagwörter	Statistical Analysis; Growth Models; Simulation; Structural Equation Models; Longitudinal Studies + Suchen Sie Ihr Suchwort? Statistische Analyse; Simulation program; Simulationsprogramm; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung
Abstract	This study investigated the optimal strategy for model specification search under the latent growth modeling (LGM) framework, specifically on searching for the correct polynomial mean or average growth model when there is no a priori hypothesized model in the absence of theory. In this simulation study, the effectiveness of different starting models on the search of the true mean growth model was investigated in terms of the mean and within-subject variance-covariance (V-C) structure model. The results showed that specifying the most complex (i.e., unstructured) within-subject V-C structure with the use of LRT, ?AIC, and ?BIC achieved the highest recovery rate (>85%) of the true mean trajectory. Implications of the findings and limitations are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01