Modeling Count Data using Stata

This course provides a comprehensive understanding of count models, equipping you with the skills to analyze count data effectively. You'll receive valuable resources including an e-book and a set of helpful slides to support your learning journey.

The course is structured into two distinct parts.

Part 1: Count Model Theory

This section lays a solid theoretical foundation for understanding count data analysis. We present complex concepts in an intuitive manner, minimizing mathematical complexity to maximize comprehension. You'll begin with a clear introduction to count tables and learn how to calculate the crucial incidence-rate ratio. Next, delve into Poisson regression, mastering the techniques for incorporating continuous, binary, and categorical variables into your models. We'll then address the common challenge of overdispersion and explore the application of negative binomial models to handle this issue. Finally, we'll cover other important count models, including truncated models and zero-inflated models, providing you with a versatile toolkit for your analyses.

Part 2: Practical Application with Stata

This part focuses on practical application using Stata, a leading statistical software package. You'll follow along with a large, real-world project to gain hands-on experience fitting Poisson, negative binomial, and zero-inflated models. Crucially, you'll learn the techniques for comparing these different models to determine the best fit for your data. This practical component ensures you can confidently apply your theoretical knowledge to solve real-world problems.