eBook - PDF
Regression Models for Categorical, Count, and Related Variables
An Applied Approach
- 432 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Regression Models for Categorical, Count, and Related Variables
An Applied Approach
About this book
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. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner.
This book provides an introduction and overview of several statistical models designed for these types of outcomes—all presented with the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis.
Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis.
Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data.
A companion website includes downloadable versions of all the data sets used in the book.
This book provides an introduction and overview of several statistical models designed for these types of outcomes—all presented with the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis.
Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis.
Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data.
A companion website includes downloadable versions of all the data sets used in the book.
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Regression Models for Categorical, Count, and Related Variables by Dr. John P. Hoffmann in PDF and/or ePUB format, as well as other popular books in Social Sciences & Social Science Research & Methodology. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Regression Models for Categorical, Count, and Related Variables
- Title
- Copyright
- Dedication
- 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
- Appendix B: Using Simulations to Examine Assumptions of OLS Regression
- Appendix C: Working with Missing Data
- References
- Index