Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market.
Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market.
Razia Azen is Professor at the University of Wisconsin-Milwaukee, USA, where she teaches basic and advanced statistics courses. Her research focuses on methods that compare the relative importance of predictors in linear models. She received her MS in statistics and PhD in quantitative psychology from the University of Illinois, USA. Cindy M. Walker is President and CEO of Research Analytics Consulting, LLC. Previously, she was a professor at the University of Wisconsin-Milwaukee, where she taught basic and advanced measurement and statistics courses. Her research focuses on applied issues in psychometrics. She received her PhD in quantitative research methodologies from the University of Illinois at Urbana-Champaign, USA.
Inhaltsangabe
Preface 1. Introduction and Overview 2. Probability Distributions 3. Proportions, Estimation, and Goodness-of-Fit 4. Association between Two Categorical Variables 5. Associations between Three Categorical Variables 6. Modeling and the Generalized Linear Model 7. Log-Linear Models 8. Logistic Regression with Continuous Predictors 9. Logistic Regression with Categorical Predictors 10. Logistic Regression for Multicategory Outcomes 11. Generalized Linear Mixed Models References
Preface 1. Introduction and Overview 2. Probability Distributions 3. Proportions, Estimation, and Goodness-of-Fit 4. Association between Two Categorical Variables 5. Associations between Three Categorical Variables 6. Modeling and the Generalized Linear Model 7. Log-Linear Models 8. Logistic Regression with Continuous Predictors 9. Logistic Regression with Categorical Predictors 10. Logistic Regression for Multicategory Outcomes 11. Generalized Linear Mixed Models References
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