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A Latent Class Approach to Fitting the Weighted Euclidean Model, Clascal

Published online by Cambridge University Press:  01 January 2025

Suzanne Winsberg and
Geert De Soete
Suzanne Winsberg
Affiliation:
IRCAM, Paris, France
Geert De Soete*
Affiliation:
University of Ghent, Belgium
*
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 weighted Euclidean distance model for analyzing three-way proximity data is proposed that incorporates a latent class approach. In this latent class weighted Euclidean model, the contribution to the distance function between two stimuli is per dimension weighted identically by all subjects in the same latent class. This model removes the rotational invariance of the classical multidimensional scaling model retaining psychologically meaningful dimensions, and drastically reduces the number of parameters in the traditional INDSCAL model. The probability density function for the data of a subject is posited to be a finite mixture of spherical multivariate normal densities. The maximum likelihood function is optimized by means of an EM algorithm; a modified Fisher scoring method is used to update the parameters in the M-step. A model selection strategy is proposed and illustrated on both real and artificial data.

Keywords

weighted Euclidean distance modelINDSCALlatent class analysismixture distribution modelEM algorithm

Information

Type
Original Paper
Information
Psychometrika , Volume 58 , Issue 2 , June 1993, pp. 315 - 330
Copyright
Copyright © 1993 The Psychometric Society

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Footnotes

The second author is supported as “Bevoegdverklaard Navorser” of the Belgian “Nationaal Fonds voor Wetenschappelijk Onderzoek”.

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