Chapter 5

Multinomial Logit Analysis

5.1   Introduction

5.2   Example

5.3   A Model for Three Categories

5.4   Estimation with PROC LOGISTIC

5.5   Estimation with a Binary Logit Procedure

5.6   General Form of the Model

5.7   Contingency Table Analysis

5.8   Problems of Interpretation

 

5.1 Introduction

Binary logistic regression 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 ...

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