This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields.

This book provides guidelines and fully worked examples of how to select, construct, interpret and evaluate the full range of count models.

Frontmatter pp i-iv Contents pp v-x Preface pp xi-xvi By Joseph M. , Arizona State University Chapter 1 - Varieties of Count Data pp 1-34 Chapter 2 - Poisson Regression pp 35-73 Chapter 3 - Testing Overdispersion pp 74-107 Chapter 4 - Assessment of Fit pp 108-125 Chapter 5 - Negative Binomial Regression pp 126-161 Chapter 6 - Poisson Inverse Gaussian Regression pp 162-171 Chapter 7 - Problems with Zeros pp 172-209 Chapter 8 - Modeling Underdispersed Count Data – Generalized Poisson pp 210-216 Chapter 9 - Complex Data: More Advanced Models pp 217-254 Appendix: SAS Code pp 255-268 Bibliography pp 269-276 Index pp 277-283