For the analysis of longitudinal data, three families of models are generally distinguished: the marginal, the transitional and the subject-specific family. In this paper, we will propose a transitional model for the analysis of change for a nominal response variable. Such an analysis is often hampered by the dimensionality of the problem. We use multidimensional scaling techniques, more specifically the ideal point model, in order to reduce the dimensionality. The model can handle pure transitional data but also allows for explanatory variables. Two empirical examples will be discussed in order to illustrate all the virtues of the model.
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