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

Published online by Cambridge University Press:  01 January 2025

Sylvie Tchumtchoua  and
Dipak K. Dey
Sylvie Tchumtchoua*
Affiliation:
Statistical and Applied Mathematical Sciences Institute
Dipak K. Dey
Affiliation:
Department of Statistics, University of Connecticut
*
Requests for reprints should be sent to Sylvie Tchumtchoua, Statistical and Applied Mathematical Sciences Institute, 19 T. W. Alexander Drive, Research Triangle Park, P.O. Box 14006, Durham, NC 27709, USA. E-mail: [email protected]
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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.

Keywords

dynamic hierarchical Dirichlet processfactor analysishierarchical factor analysishigh-dimensional datalongitudinal categorical variables
Type
Original Paper
Information
Psychometrika , Volume 77 , Issue 4 , October 2012 , pp. 670 - 692
Copyright
Copyright © 2012 The Psychometric Society

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