| Abstract: | 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. |
