Description

Book Synopsis
Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. This book provides an introduction and overview of several statistical models designed for these types of outcomes.

Table of Contents
Preface Acknowledgments 1. Review of Linear Regression Models 2. Categorical Data and Generalized Linear Models 3. Logistic and Probit Regression Models 4. Ordered Logistic and Probit Regression Models 5. Multinomial Logistic and Probit Regression Models 6. Poisson and Negative Binomial Regression Models 7. Event History Models 8. Regression Models for Longitudinal Data 9. Multilevel Regression Models 10. Principal Components and Factor Analysis Appendix A: SAS, SPSS, and R Code for Examples in Chapters Section 1: SAS Code Section 2: SPSS Syntax Section 3: R Code Appendix B: Using Simulations to Examine Assumptions of OLS Regression Appendix C: Working with Missing Data References Index

Regression Models for Categorical Count and

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    A Paperback / softback by Dr. John P. Hoffmann

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      Book details

      Publisher University of California Press
      Published 16 August 2016
      ISBN-13 9780520289291
      978-0520289291
      ISBN-10 0520289293

      Description

      Book Synopsis
      Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. This book provides an introduction and overview of several statistical models designed for these types of outcomes.

      Table of Contents
      Preface Acknowledgments 1. Review of Linear Regression Models 2. Categorical Data and Generalized Linear Models 3. Logistic and Probit Regression Models 4. Ordered Logistic and Probit Regression Models 5. Multinomial Logistic and Probit Regression Models 6. Poisson and Negative Binomial Regression Models 7. Event History Models 8. Regression Models for Longitudinal Data 9. Multilevel Regression Models 10. Principal Components and Factor Analysis Appendix A: SAS, SPSS, and R Code for Examples in Chapters Section 1: SAS Code Section 2: SPSS Syntax Section 3: R Code Appendix B: Using Simulations to Examine Assumptions of OLS Regression Appendix C: Working with Missing Data References Index

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