Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences) [Hardcover]

J. Scott Long (Author)

Book Description

ISBN-10: 0803973748 | ISBN-13: 978-0803973749 | Publication Date: January 9, 1997 | Edition: 1

A unified treatment of the most useful models for categorical and limited dependent variables (CLDVs) is provided in this book. Throughout, the links among the models are made explicit, and common methods of derivation, interpretation and testing are applied. In addition, the author explains how models relate to linear regression models whenever possible.

Editorial Reviews

Review

"Regression Models for Categorical and Limited Dependent Variables excels at explaining applications of nonlinear regression models. . . The  book provides much practical guidance for the estimation, identification, and validation of models for CLDVs. Each chapter is interspersed with exercises and helpful questions. In summary, the author exceeds his goal to provide ‘a firm foundation’ for further reading from the vast and growing literature on limited and categorical dependent variables."

(Ulf Bockenholt Chance )

Product Details

• Hardcover: 328 pages
• Publisher: Sage Publications, Inc; 1 edition (January 9, 1997)
• Language: English
• ISBN-10: 0803973748
• ISBN-13: 978-0803973749
• Product Dimensions: 9.3 x 6.4 x 1 inches
• Shipping Weight: 1.4 pounds (View shipping rates and policies)
• Average Customer Review: 4.8 out of 5 stars  See all reviews (8 customer reviews)
• Amazon Best Sellers Rank: #119,527 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

13 of 13 people found the following review helpful:

5.0 out of 5 stars Most intuitive book on the subject, August 24, 2001

By A Customer

This review is from: Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences) (Hardcover)

This book is especially useful to start understanding topics like ordered probit, multinomial logit, negative binomial regression and zero-inflated count models. Although it starts with a chapter on the linear regression model, it should not be mistaken for an introductory text. I would certainly advise readers with limited background in regression models to start with other books, like the one of Wooldridge (Introductory Econometrics). The quality of this book must be that I've yet to see a book that explains these topics more intuitively. That is not to say it is easy or without mathematics, it's not. It just looks like the mathematics is only used for better comprehension, not to give you the full proof. Furthermore, while reading it you get the feeling that the author understands what you, as a researcher, are interested in. This allows him to focus on the topics of interest, like model selection and testing and interpretation of output. So although this is not a cookbook, it may well be the closest thing to it, especially in combination with his new book on applying these models in Stata. It is a pity that the author stops short of non-parametric models (next edition?).

10 of 10 people found the following review helpful:

5.0 out of 5 stars Extremely good book on Logistic Regression, December 11, 2002

By 

Jude C. Ryan (Springfield, NJ United States) - See all my reviews
(REAL NAME)   

This review is from: Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences) (Hardcover)

Since I do statistical modeling in industry, I was looking for a good book on Logistic regression that would give me a deep understanding of the subject; one that also had wide coverage (Poison regression, Tobit models, ..etc.). I decided on J. Scott Long's book, after considering Applied Logistic Regression by Hosmer and Lemeshow, and Limited Dependent and Qualitative Variables in Econometrics by Maddala. I must say I am very pleased with my choice. The topics are very clear, and the math is used as an aid to understanding, and you don't get bogged down in the math. It is a pleasure to read the book.

5 of 5 people found the following review helpful:

4.0 out of 5 stars Useful review of ML based methods, February 24, 2001

By 

JVerkuilen (BAYSIDE, NY, United States) - See all my reviews
(REAL NAME)   

Amazon Verified Purchase

This review is from: Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences) (Hardcover)

A nice review of MLE-based methods for categorical, limited, and ordinal dependent variables. Most social science data is best thought of as categorical, ordinal, etc., not interval, and so a readable treatment of one approach to the analysis of such data that does not rely on intervality assumptions is worthwhile.

The author has a very clear explanation of topics such as how MLE works, some numerical methods for maximizing, various tests associated with MLEs, etc., all written at a intermediate level. It's not too advanced so readers won't be driven off but also isn't a cookbook. Lots of nice examples throughout.

It's definitely in standard regression mode, which is not to say bad, just limited. It doesn't cover (or indeed discuss) topics such as categorical multivariate analysis, alternate loss functions for estimating categorical or ordinal regressions, including alternating least squares approaches or quantile regression, categorical or ordinal time series, or instrumental variables.... It's not the last word on the topic, but is certainly a solid first word.