This volume helps readers understand the intuitive logic behind logistic regression through nontechnical language and simple examples. The Second Edition presents results from several statistical packages to help interpret the meaning of logistic regression coefficients, presents more detail on variations in logistic regression for multicategory outcomes, and describes some potential problems in interpreting logistic regression coefficients. A companion website includes the three data sets and Stata, SPSS, and R commands needed to reproduce all the tables and figures in the book. Finally, the Appendix reviews the meaning of logarithms, and helps readers understand the use of logarithms in logistic regression as well as in other types of models.

Ordinal and Multinomial Logistic Regression

Ordinal and Multinomial Logistic Regression

The previous chapters aimed to explain the basic principles underlying logistic regression (and the companion probit analysis) rather than to offer a comprehensive description or mathematical derivation of the techniques. Understanding the basic principles, however, can help to master more complex and advanced topics. In particular, the logic of the logit transformation and maximum likelihood estimation in binary logistic regression applies to models for dependent variables with three or more categories. To emphasize the generality of the underlying principles presented so far and to offer an introduction to more advanced material, this chapter extends the binary logistic regression model.

The focus here is on two commonly used models for outcomes with three or more categories—one with ordinal or ordered categories ...

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