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A latent class probit model for analyzing pick any/N data

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Abstract

A latent class probit model is developed in which it is assumed that the binary data of a particular subject follow a finite mixture of multivariate Bermoulli distributions. An EM algorithm for fitting the model is described and a Monte Carlo procedure for testing the number of latent classes that is required for adequately describing the data is discussed. In the final section, an application of the latent class probit model to some intended purchase data for residential telecommunication devices is reported.

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Author information

Authors and Affiliations

  1. Department of Psychology, University of Ghent, Henri Dunantlaan 2, B-9000, Ghent, Belgium

    Geert De Soete

  2. Marketing and Statistics Departments, Graduate School of Business, University of Michigan, 48109, Ann Arbor, MI, USA

    Wayne S. DeSarbo

Authors
  1. Geert De Soete
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  2. Wayne S. DeSarbo
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Additional information

Geert De Soete is supported as “Bevoegdverklaard Navorser” of the Belgian “Nationaal Fonds voor Wetenschappelijk Onderzoek.”

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De Soete, G., DeSarbo, W.S. A latent class probit model for analyzing pick any/N data. Journal of Classification 8, 45–63 (1991). https://doi.org/10.1007/BF02616247

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  • DOI: https://doi.org/10.1007/BF02616247

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