Latent-class scaling models for the analysis of longitudinal choice data
A new class of multidimensional scaling models for the analysis of longitudinal choice data is introduced. This class of models extends the work by Böckenholt and Böckenholt (1991) who proposed a synthesis of latent-class and multidimensional scaling (mds) models to take advantage of the attractive features of both classes of models. The mds part provides graphical representations of the choice data while the latent-class part yields a parsimonious but still general representation of individual differences. The extensions discussed in this paper involve simultaneously fitting the mds latent-class model to the data obtained at each time point and modeling stability and change in preferences by autocorrelations and shifts in latent-class membership over time.
KeywordsLatent Class Ideal Point Vector Model Preference Vector Perceptual Space
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