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Models to investigate categorical data can be divided into preprocessing, limited parameterization, and formal logit models. To illustrate the advantages of preprocessing and limited parameterization models they are applied to a data set of tenure and type of housing choice before the data are examined with hierarchical logit and nested logit models. The preprocessing approaches are useful in selecting optimal subsets of independent variables with respect to the dependent variable. The ease of application and interpretation of a limited parameterization approach extends the clarity of the results from the preprocessing approaches. Because some variables are only relevant at specific levels of other independent variables, nonstandard (nested) logit models are necessary to understand the nested relationships. Techniques that use categorical data have proliferated in the past decade. Logit, mutinomial logit, and sequential logit models have been applied to mobility (Clark, Deurloo, and Dieleman 1984; Clark and Onaka 1985), consumer choice (Wrigley 1985), housing choice (Quigley 1976), and transportation mode choice (Johnson and Hensher 1982) to mention only a few of the substantive areas. However, this work has sometimes proceeded without a detailed consideration of the underlying contingency tables. In this article, we deal with the statistical analysis of contingency tables rather than with sequential choice modeling. It is necessary to stress this point because both statistical modeling of categorical data and discrete choice approaches often use the same terms with different meanings. An example of this is the term nested logit model. In a later section, the term is not used for sequential choice modeling, as is usually the case in geographical studies (Wrigley 1985), but to describe the situation where the effect of an independent variable (on the dependent variable) is only operative |
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