STATISTICS COLLOQUIUM
Steven Andrew Culpepper, Associate Professor
Department of Statistics
Beckman Institute for Advanced Science and Technology
University of Illinois at Urbana-Champaign
Inferring latent structure in polytomous data
Abstract
Researchers continue to develop and advance latent structure models (LSMs) for research in the social and behavioral sciences. LSMs provide researchers with a framework for providing a fine-grained classification of respondents into substantively meaningful latent classes. Recent research developed confirmatory LSMs for polytomous response data; however, these methods require detailed knowledge about the relationship between the multivariate latent attributes and observed responses. This study advances existing methods by proposing new exploratory methods for inferring the latent structure underlying polytomous response data. We discuss new sufficient conditions for ensuring model parameter identifiability. A novel Bayesian formulation is presented using a variable selection algorithm. An application to the 2012 Programme for International Student Assessment (PISA) problem-solving vignettes is presented to demonstrate the method. We conclude with a brief overview of future research directions for broadening the application of LSMs in social, behavioral, and health research.
For more information, contact: Tracy Burke at tracy.burke@uconn.edu