Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences) by J. Scott Long
Comment from the Stata technical group
While regression models for categorical dependent variables are ubiquitous, a discussion of how to interpret these models has been sorely lacking. Regression Models for Categorical Dependent Variables Using Stata, Revised Edition, fills this void. This book discusses how to fit and interpret regression models for categorical data with Stata and includes some commands written by the authors. Hypothesis testing and goodness-of-fit statistics are also discussed.
The book begins with a lucid introduction to Stata and then provides a general treatment of estimation, testing, fit, and interpretation in this class of models. Binary, ordinal, nominal, and count outcomes are covered in detail in separate chapters. The final chapter discusses how to fit and interpret models with special characteristics, such as ordinal and nominal independent variables, interaction, and nonlinear terms. One appendix discusses the syntax of the author-written commands, and a second gives details of the datasets used by the authors in the book.
This book is filled with concrete examples. Because all the examples, datasets, and author-written commands are available from the authors at their web site, readers can easily replicate the examples using Stata. This book is ideal for students or applied researchers who want to know how to fit this type of model and understand its output.
The revised edition uses the new Stata graphics system throughout the book. In addition, the revised edition discusses multiple missing-value codes and contains updated output throughout the text.