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Regression Models for Categorical Dependent Variables Using Stata, Third Edition
Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression models for categorical data. The third edition is a complete rewrite of the book. Factor variables and the margins command changed how the effects of variables can be estimated and interpreted. In addition, the authors' views on interpretation have evolved. The changes to Stata and to the authors' views inspired the authors to completely rewrite their popular SPost commands to take advantage of the power of the margins command and the flexibility of factor-variable notation. The new edition will interest readers of a previous edition as well as new readers. Even though about 150 pages of appendixes were removed, the third edition is about 60 pages longer than the second.
Although regression models for categorical dependent variables are common, few texts explain how to interpret such models; this text fills the void. With the book, Long and Freese provide a suite of commands for model interpretation, hypothesis testing, and model diagnostics. The new commands that accompany the third edition make it easy to include powers or interactions of covariates in regression models and work seamlessly with models estimated with complex survey data.
The authors' new commands greatly simplify the use of margins, in the same way that the marginsplot command harnesses the power of margins for plotting predictions. The authors discuss how to use margins and their new mchange, mtable, and mgen commands to compute tables and to plot predictions. They also discuss how to use these commands to estimate marginal effects, averaged either over the sample or at fixed values of the regressors. The authors introduce and advocate a variety of new methods that use predictions to interpret the effect of variables in regression models.
The third edition begins with an excellent introduction to Stata and follows with general treatments of the estimation, testing, fit, and interpretation of this class of models. New to the third edition is an entire chapter about how to interpret regression models using predictions--a chapter that is expanded upon in later chapters that focus on models for binary, ordinal, nominal, and count outcomes.
Long and Freese use many concrete examples in their third edition. All the examples, datasets, and author-written commands are available on the authors' website, so readers can easily replicate the examples with Stata. This book is ideal for students or applied researchers who want to learn how to fit and interpret models for categorical data.
Although regression models for categorical dependent variables are common, few texts explain how to interpret such models; this text fills the void. With the book, Long and Freese provide a suite of commands for model interpretation, hypothesis testing, and model diagnostics. The new commands that accompany the third edition make it easy to include powers or interactions of covariates in regression models and work seamlessly with models estimated with complex survey data.
The authors' new commands greatly simplify the use of margins, in the same way that the marginsplot command harnesses the power of margins for plotting predictions. The authors discuss how to use margins and their new mchange, mtable, and mgen commands to compute tables and to plot predictions. They also discuss how to use these commands to estimate marginal effects, averaged either over the sample or at fixed values of the regressors. The authors introduce and advocate a variety of new methods that use predictions to interpret the effect of variables in regression models.
The third edition begins with an excellent introduction to Stata and follows with general treatments of the estimation, testing, fit, and interpretation of this class of models. New to the third edition is an entire chapter about how to interpret regression models using predictions--a chapter that is expanded upon in later chapters that focus on models for binary, ordinal, nominal, and count outcomes.
Long and Freese use many concrete examples in their third edition. All the examples, datasets, and author-written commands are available on the authors' website, so readers can easily replicate the examples with Stata. This book is ideal for students or applied researchers who want to learn how to fit and interpret models for categorical data.
589 pages, Paperback
First published January 1, 2001
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Displaying 1 - 11 of 11 reviews
November 2, 2020
Really solid manual on categorical regression models. Easy to go through with examples along the way.
April 28, 2024
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.
April 11, 2018
This book is less useful as an introduction to regression models and more useful as a reference when performing different regressions in Stata.
December 16, 2019
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.
February 10, 2016
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.
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.
October 15, 2009
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.
August 17, 2012
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.
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November 4, 2008statistical analysis
April 4, 2009
Best book for categorical dependent variables. I gained alot from it.
June 17, 2012
One of the best references I've read about analysing categorical dependent variables using STATA. Simple, comprehensive and nicely structured.
November 8, 2014
Very good read on the subject. Very helpful!
Displaying 1 - 11 of 11 reviews