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9780803973749 Academic Inspection Copy

Regression Models for Categorical and Limited Dependent Variables

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After reviewing the linear regression model and introducing maximum likelihood estimation, Long extends the binary logit and probit models, presents multinomial and conditioned logit models and describes models for sample se lection bias. '
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.
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 *
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