In this chapter we focus on the Logit model and the Probit model for an ordered dependent variable, where this variable is not continuous but takes discrete values. Such an ordered multinomial variable differs from an unordered variable by the fact that individuals now face a ranked variable. Examples of ordered multinomial data typically appear in questionnaires, where individuals are, for example, asked to indicate whether they strongly disagree, disagree, are indifferent, agree or strongly agree with a certain statement, or where individuals have to evaluate characteristics of a (possibly hypothetical) brand or product on a five-point Likert scale. It may also be that individuals themselves are assigned to categories, which sequentially concern a more or less favorable attitude towards some phenomenon, and that it is then of interest to the market researcher to examine which explanatory variables have predictive value for the classification of individuals into these categories. In fact, the example in this chapter concerns this last type of data, where we analyze individuals who are all customers of a financial investment firm and who have been assigned to three categories according to their risk profiles. Having only bonds corresponds with low risk and trading in financial derivatives may be viewed as more risky. It is the aim of this empirical analysis to investigate which behavioral characteristics of the individuals can explain this classification.

The econometric models which are useful for such an ordered dependent variable are called ordered regression models. Examples of applications in marketing research usually concern customer satisfaction, perceived customer value and perceptual mapping (see, for example, Katahira, 1990, and Zemanek, 1995, among others).