Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Preface
- Acknowledgements
- Notes on Notation
- Part I Concepts, Theory, and Implementation
- Part II Model Evaluation and Interpretation
- Part III The Generalized Linear Model
- 7 The Generalized Linear Model
- 8 Ordered Categorical Variable Models
- 9 Models for Nominal Data
- 10 Strategies for Analyzing Count Data
- Part IV Advanced Topics
- Part V A Look Ahead
- Bibliography
- Index
10 - Strategies for Analyzing Count Data
from Part III - The Generalized Linear Model
Published online by Cambridge University Press: 15 November 2018
Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Preface
- Acknowledgements
- Notes on Notation
- Part I Concepts, Theory, and Implementation
- Part II Model Evaluation and Interpretation
- Part III The Generalized Linear Model
- 7 The Generalized Linear Model
- 8 Ordered Categorical Variable Models
- 9 Models for Nominal Data
- 10 Strategies for Analyzing Count Data
- Part IV Advanced Topics
- Part V A Look Ahead
- Bibliography
- Index
Summary
Applies the GLM framework to modeling event count data. Discusses the common problem of overdispersion and the methods for extending the model to account for it.
- Type
- Chapter
- Information
- Maximum Likelihood for Social ScienceStrategies for Analysis, pp. 190 - 216Publisher: Cambridge University PressPrint publication year: 2018