Bayesian Analysis of Longitudinal Data Using Growth Curve Models

Autor/inn/en	Zhang, Zhiyong; Hamagami, Fumiaki; Wang, Lijuan Lijuan; Nesselroade, John R.; Grimm, Kevin J.
Titel	Bayesian Analysis of Longitudinal Data Using Growth Curve Models
Quelle	In: International Journal of Behavioral Development, 31 (2007) 4, S.374-383 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0165-0254
DOI	10.1177/0165025407077764
Schlagwörter	Computation; Bayesian Statistics; Statistical Analysis; Longitudinal Studies; Models; Data Analysis; National Longitudinal Survey of Youth + Suchen Sie Ihr Suchwort? Statistische Analyse; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Analogiemodell; Auswertung
Abstract	Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data from the National Longitudinal Survey of Youth. This step-by-step example illustrates how to analyze data using both noninformative and informative priors. The results show that in addition to being an alternative to the maximum likelihood estimation (MLE) method, Bayesian methods also have unique strengths, such as the systematic incorporation of prior information from previous studies. These methods are more plausible ways to analyze small sample data compared with the MLE method. (Contains 3 figures, 3 tables and 7 footnotes.) (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10