1 Introduction


This website houses all the information you need learn the basics of coding a number of different categorical and count models in Stata and R. It will not contain all the information taught in class, but will allow you to bridge that knowledge into running these models on your own. The Stata labs on this website were adapted from materials by Ewurama Okai.

1.1 Lab Structure

This course will contain 8 labs and an optional review lab at the end of the course. Our lab sessions will alternate between learning the regression models covered in the course and fundamental coding skills. We will also be including time for you to workshop your final projects before the end of the course.


Each lab will contain links to download script files (in .do or .r format) and overviews of key concepts.

Lab Topics

Note: All lab topics are tentative and subject to change.

  • Lab 1: Introduction
  • Lab 2: Linear Probability Models
  • Lab 3: Logistic Regression
  • Lab 4: Fundamentals Review
  • Lab 5: Fundamentals Review + Likelihood Ratio Tests
  • Lab 6: Probit Regression
  • Lab 7: Multinomial Logistic Regression & Ordinal Regression
  • Lab 8: Poisson Regression & Negative Binomial Regression
  • Lab 9: Optional Review

1.2 Finding Data

When selecting data, consider:

  • The research question you would like to answer
  • Whether the dataset contains a categorical or count outcome variable (to fit the models we will be learning in this class)
  • The unit/level of analysis in the dataset (individual? school? district? state?)
  • The main independent and dependent variables you want to analyze
  • Other relevant variables to include in your model

Some places to find datasets:

1.3 Stata Resources