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

Sylvie Tchumtchoua () and Dipak Dey ()

Psychometrika, 2012, vol. 77, issue 4, 670-692

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. Copyright The Psychometric Society 2012

Keywords: dynamic hierarchical Dirichlet process; factor analysis; hierarchical factor analysis; high-dimensional data; longitudinal categorical variables (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s11336-012-9274-4

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