Regression Models for Categorical and Limited Dependent Variables / Edition 1

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Overview

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.

Linear probability, binary outcomes, hypothesis testing & goodness of fit, ordinal outcomes, nominal outcomes, etc.

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Editorial Reviews

Chance
"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 - Ulf Bockenholt

"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."

Booknews
Evaluates the most useful models for categorical and limited dependent variables CLDVs, emphasizing the links among models and applying common methods of derivation, interpretation, and testing. The author also explains how models relate to linear regression models whenever possible. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

Meet the Author

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.
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Table of Contents

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