5.1. Introduction
Binary logit analysis is ideal when your dependent variable has two categories, but what if it has three or more? In some cases, it may be reasonable to collapse categories so that you have only two, but that strategy inevitably involves some loss of information. In other cases, collapsing categories could seriously obscure what you’re trying to study. Suppose you want to estimate a model predicting whether newly registered voters choose to register as Democrats, Republicans, or Independents. Combining any two of these outcomes could lead to seriously misleading conclusions.
How you deal with such situations depends somewhat on the nature of the outcome variable and the goal of the analysis. If the categories of the dependent ...
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