Regression Models for Categorical Dependent Variables Using Stata, Third Edition
by J. Scott Long, Jeremy Freese
Regression Models for Categorical Dependent Variables Using Stata, Third Edition, by J. Scott Long and Jeremy Freese, delivers in-depth coverage of statistical techniques for analyzing categorical outcomes. It guides users through model specification, estimation, and interpretation in Stata, with practical examples for social science applications. Ideal for researchers seeking precise methods for binary, ordinal, and nominal data.
About This Book
This book offers detailed instruction on applying regression models to categorical dependent variables within the Stata software environment. The third edition builds on previous versions with updated examples and expanded coverage of advanced techniques.
Authors J. Scott Long and Jeremy Freese explain the theoretical foundations and practical implementation of models such as logistic regression for binary outcomes and multinomial logit for nominal variables.
Designed for researchers and students in sociology, economics, and related fields, it includes step-by-step guidance on model estimation, interpretation, and diagnostics using Stata commands.
The text emphasizes robust methods to handle common issues like heteroskedasticity and clustered data in categorical analysis.
With numerous real-world examples, it serves as a key resource for conducting reliable statistical analyses in empirical research.
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