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Psychometrika

, Volume 58, Issue 2, pp 315–330 | Cite as

A latent class approach to fitting the weighted Euclidean model, clascal

  • Suzanne Winsberg
  • Geert De Soete
Article

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.

Key words

weighted Euclidean distance model INDSCAL latent class analysis mixture distribution model EM algorithm 

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Copyright information

© The Psychometric Society 1993

Authors and Affiliations

  • Suzanne Winsberg
    • 1
  • Geert De Soete
    • 2
  1. 1.IRCAMParisFrance
  2. 2.Department of Data AnalysisUniversity of GhentGhentBelgium

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