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Regression Models for Categorical and Limited Dependent Variables
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Regression Models for Categorical and Limited Dependent Variables



January 1997 | 328 pages | SAGE Publications, Inc
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.

After a review of the linear regression model and an introduction to maximum likelihood estimation, the book then: covers the logit and probit models for binary outcomes; reviews standard statistical tests associated with maximum likelihood estimation; and considers a variety of measures for assessing the fit of a model. J Scott Long also: extends the binary logit and probit models to ordered outcomes; presents the multinomial and conditioned logit models for nominal outcomes; considers models with censored and truncated dependent variables with a focus on the tobit model; describes models for sample selection bias; presents models for count outcomes by beginning with the Poisson regression model; and compares the models from earlier chapters, discussing the links between these models and others not discussed in the book.

 
Introduction
 
Continuous Outcomes
 
Binary Outcomes
 
Testing and Fit
 
Ordinal Outcomes
 
Nominal Outcomes
 
Limited Outcomes
 
Count Outcomes
 
Conclusions

"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

Stellar explanation of generalized linear models.

Dr Joseph Campbell
Demography, University of Texas at San Antonio
June 22, 2012

This is a very well-organized, complete treatment of the analysis of limited qualitiative dependent variables. It also has a complimentary text (which unfortunately is not published by Sage) that guides for exercises with Stata.

Professor Lorena Barberia
Ciência Política , Universidade de São Paulo
May 31, 2012

The book gives a deep insight into the models of binary and categorical variables. The theoretical work is well done and is therefore ideal as an accompanying textbook.

Mr Christian Pfarr
Law and Economics, University of Bayreuth
May 4, 2010

The book is a nice introduction to the subject material and is largely accessible even to students with little college-level mathematics training. The subject matter is presented clearly and in a manner useful to applied researchers.

Dr David Armstrong
Political Science Dept, University of Wisconsin - Milwaukee
January 6, 2010

J. Scott Long

Scott Long is Distinguished Professor and Chancellor's Professor of Sociology and Statistics at Indiana University, Bloomington. He teaches quantitative methods both at Indiana University and at the ICSPR Summer Program. His earlier research examined gender differences in the scientific career. In recent years, he has collaborated with Eliza Pavalko, Bernice Pescsolido, John Bancroft, Julia Heiman and others in studies of health and aging, stigma and mental health, and human sexuality. More About Author

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ISBN: 9780803973749
$ 100.00