Modeling Count Data is written for the practicing researcher who has a reason to analyze and draw sound conclusions from modeling count data. More specifically, it is written for an analyst who needs to construct a count response model but is not sure how to proceed.

A count response model is a statistical model for which the dependent, or response, variable is a count. A count is understood as a nonnegative discrete integer ranging from zero to some specified greater number. This book aims to be a clear and understandable guide to the following points:

1. • How to recognize the characteristics of count data

2. • Understanding the assumptions on which a count model is based

3. • Determining whether data violate these assumptions (e.g., overdispersion), why this is so, and what can be done about it

4. • Selecting the most appropriate model for the data to be analyzed

5. • Constructing a well-fitted model

6. • Interpreting model parameters and associated statistics

7. • Predicting counts, rate ratios, and probabilities based on a model

8. • Evaluating the goodness-of-fit for each model discussed

There is indeed a lot to consider when selecting the best-fitted model for your data. I will do my best in these pages to clarify the foremost concepts and problems unique to modeling counts. If you follow along carefully, you should have a good overview of the subject and a basic working knowledge needed for constructing an appropriate model for your study data.