Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

Autor/inn/en	Tchumtchoua, Sylvie; Dey, Dipak K.
Titel	Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach
Quelle	In: Psychometrika, 77 (2012) 4, S.670-692 (23 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-012-9274-4
Schlagwörter	Factor Analysis; Bayesian Statistics; Behavioral Sciences; Social Sciences; Markov Processes; Longitudinal Studies; Surveys; Public Opinion; Technology; Scientific Attitudes; Attitude Measures; Psychometrics; Models; Data Analysis; Nonparametric Statistics; Correlation + Suchen Sie Ihr Suchwort? Faktorenanalyse; Social science; Sozialwissenschaften; Gesellschaftswissenschaften; Markowscher Prozess; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Survey; Umfrage; Befragung; Öffentliche Meinung; Technologie; Psychometry; Psychometrie; Analogiemodell; Auswertung; Korrelation
Abstract	This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the distributions of the factors are modeled nonparametrically through a dynamic hierarchical Dirichlet process prior. A Markov chain Monte Carlo algorithm is developed for fitting the model, and the methodology is exemplified through a study of the dynamics of public attitudes toward science and technology in the United States over the period 1992-2001. (Contains 5 tables and 7 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10