Abstract
A reparameterization of a latent class model is presented to simultaneously classify and scale nominal and ordered categorical choice data. Latent class-specific probabilities are constrained to be equal to the preference probabilities from a probabilistic ideal-point or vector model that yields a graphical, multidimensional representation of the classification results. In addition, background variables can be incorporated as an aid to interpreting the latent class-specific response probabilities. The analyses of synthetic and real data sets illustrate the proposed method.
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The authors thank Yosiho Takane, the editor and referees for their valuable suggestions. Authors are listed in reverse alphabetical order.
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Böckenholt, U., Böckenholt, I. Constrained latent class analysis: Simultaneous classification and scaling of discrete choice data. Psychometrika 56, 699–716 (1991). https://doi.org/10.1007/BF02294500
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DOI: https://doi.org/10.1007/BF02294500