Regression Models for Categorical Dependent Variables Using Stata

by J. Scott Long

Although regression models for categorical dependent variables are common, few texts explain how to interpret such models. Regression Models for Categorical Dependent Variables Using Stata, Second Edition, fills this void, showing how to fit and interpret regression models for categorical data with Stata. The authors also provide a suite of commands for hypothesis testing and model diagnostics to accompany the book.The book begins with an excellent introduction to Stata and then provides a general treatment of estimation, testing, fit, and interpretation in this class of models. It covers in detail binary, ordinal, nominal, and count outcomes 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.Nearly 50% longer than the previous edition, the book covers new topics for fitting and interpreting models included in Stata 9, such as multinomial probit models, the stereotype logistic model, and zero-truncated count models. Many of the interpretation techniques have been updated to include interval as well as point estimates.New to the Second

368 pages, Paperback

First published January 1, 2001

Reviews

[Connor Oswald]

Really solid manual on categorical regression models. Easy to go through with examples along the way.

[Marks54]

I do not normally review manuals/texts like this but I am going through this for a large current project and have found it to be exceptionally useful for getting up to speed on these odd models on STATA.

[Rachel]

This book is less useful as an introduction to regression models and more useful as a reference when performing different regressions in Stata.

[Juan Gonzalez]

Un libro genial para aprender a hacer distintos tipos de regresiones en Stata. Lo mejor es acompañarlo con la lectura de Microeconometrics de Cameron & Trivedi para ver un poco más de trasfondo estadístico y comparar entre modelos.

[Lissa]

A helpful and detailed book, but the downloadable mtable command for predicted probabilities in Stata was quirky. It worked best (without errors) when most rows were displayed as their own table. I would recommend that when using the mtable command, number and specifically label each "row" as the command sometimes displayed the rows out of order or with duplicates in a table, making it hard to tell which predicted probability was for what variable categories. Another quirk was that when mtable is installed, you can no longer use the older probability commands, so that was an unexpected pain.

Other than mtable, I have no complaints. Since I was just doing logistic and multinomial logistic regression for my dissertation, I didn't delve too deeply into other sections of the book. A class I TA'd for did use the text and the students seemed to find it helpful. They just used it for logistic regression.

[Isabelrt]

I have the 1st edition, so it may be slightly different. Still, a most useful book. Clear and to the point, with enough theoretical references so you may look up what you need, but sticking to the "how to" of things. Good examples and clear explanations. The best book I ever bought in grad school.

[Stephen Cranney]

It simultaneously explains both the theoretical background behind the statistical procedures and the Stata syntax involved to execute the procedures; so it's perfect for people wanting to learn the methods for practical analytical purposes.

[Star]

statistical analysis

[Asif]

Best book for categorical dependent variables. I gained alot from it.

[Sultan Almalki]

One of the best references I've read about analysing categorical dependent variables using STATA. Simple, comprehensive and nicely structured.

[Lin]

Very good read on the subject. Very helpful!