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A Latent Class Unfolding Model for Analyzing Single Stimulus Preference Ratings

Published online by Cambridge University Press:  01 January 2025

Geert De Soete and
Willem J. Heiser
Geert De Soete*
Affiliation:
University of Ghent, Belgium
Willem J. Heiser
Affiliation:
University of Leiden, The Netherlands
*
Requests for reprints should be sent to Geert De Soete, Department of Data Analysis, University of Ghent, Henri Dunantlaan 2, B-9000 Ghent, Belgium.
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Abstract

A multidimensional unfolding model is developed that assumes that the subjects can be clustered into a small number of homogeneous groups or classes. The subjects that belong to the same group are represented by a single ideal point. Since it is not known in advance to which group or class a subject belongs, a mixture distribution model is formulated that can be considered as a latent class model for continuous single stimulus preference ratings. A GEM algorithm is described for estimating the parameters in the model. The M-step of the algorithm is based on a majorization procedure for updating the estimates of the spatial model parameters. A strategy for selecting the appropriate number of classes and the appropriate number of dimensions is proposed and fully illustrated on some artificial data. The latent class unfolding model is applied to political science data concerning party preferences from members of the Dutch Parliament. Finally, some possible extensions of the model are discussed.

Keywords

multidimensional unfoldingSMACOFmajorization methodlatent class analysismixture distribution modelEM algorithm

Information

Type
Original Paper
Information
Psychometrika , Volume 58 , Issue 4 , December 1993, pp. 545 - 565
Copyright
Copyright © 1993 The Psychometric Society

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Footnotes

The first author is supported as “Bevoegdverklaard Navorser” of the Belgian “Nationaal Fonds voor Wetenschappelijk Onderzoek”. Part of this paper was presented at the Distancia meeting held in Rennes, France, June 1992.

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