Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data

Autor/inn/en	Lu, Zhenqiu Laura; Zhang, Zhiyong; Lubke, Gitta
Titel	Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data
Quelle	In: Multivariate Behavioral Research, 46 (2011) 4, S.567-597 (31 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0027-3171
Schlagwörter	Bayesian Statistics; Statistical Inference; Computation; Models; Sampling; Markov Processes; Nonverbal Ability; Mathematics; Data Analysis; Longitudinal Studies; National Longitudinal Survey of Youth + Suchen Sie Ihr Suchwort? Inferential statistics; Schließende Statistik; Analogiemodell; Markowscher Prozess; Mathematik; Auswertung; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung
Abstract	"Growth mixture models" (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class probabilities depend on some observed explanatory variables and data missingness depends on both the explanatory variables and a latent class variable. A full Bayesian method is then proposed to estimate the model. Through the data augmentation method, conditional posterior distributions for all model parameters and missing data are obtained. A Gibbs sampling procedure is then used to generate Markov chains of model parameters for statistical inference. The application of the model and the method is first demonstrated through the analysis of mathematical ability growth data from the National Longitudinal Survey of Youth 1997 (Bureau of Labor Statistics, U.S. Department of Labor, 1997). A simulation study considering 3 main factors (the sample size, the class probability, and the missing data mechanism) is then conducted and the results show that the proposed Bayesian estimation approach performs very well under the studied conditions. Finally, some implications of this study, including the misspecified missingness mechanism, the sample size, the sensitivity of the model, the number of latent classes, the model comparison, and the future directions of the approach, are discussed. (Contains 5 tables, 3 figures, and 8 footnotes.) (As Provided).
Anmerkungen	Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10