Categorical Data Analysis is a course in in applied statistics that deals with regression models in which the dependent variable is binary, nominal, ordinal, or count. Models considered include probit and logit for binary outcomes, ordered logit and ordered probit for ordinal outcomes, multinomial logit for nominal outcomes, and Poisson regression, negative binomial, and zero inflated models for counts. Other less common models are considered briefly. Basic algebra, probability theory, and concepts from calculus are used to explain the structure and assumptions of each model. These ideas are then used to demonstrate sophisticated methods of interpretation that deal with the complications introduced by the nonlinearity of the models. The focus of the class is on interpretation. Grading is based on participation and weekly assignments that focus on specifing, fitting and interpreting each type of model. Assignments and lectures use Stata. Here is a recent syllabus.

Enrolling in Soc 650/Stat 503 and Time Conflicts

First priority is given to students for which this is required for their degree program. Otherwise, authorizations are given on a first-come-first-serve basis. If you are interested in taking the class, contact Scott Long for authorization (jslong@indiana.edu). Please include information on your prior class on linear regression. After authorization, sign up for the class during the normal enrollment period; if you do not, your authorization might be given to a student on the wait list.

Time conflicts: If you have another class that overlaps with lectures, you will need to take the class another semester. If you have a time conflict with all of the lab times, you should take the class some other semester. If you can attend some of the labs each week and you are already familiar with Stata (or can learn it on your own), you should do fine, but might have to work harder. While most of the lab time is used for students doing independent work, the teaching assistants provide short lectures related to the assignments.

Books used in class

1. The lectures slides are provided as PDFs.

2. Long, J. Scott and Freese, Jeremy. 2014. Regression Models for Categorical Dependent Variables Using Stata, 3rd Edition. Stata Press: College Stata, TX is required. .

3. Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage is useful for those who are interested in mathematical details.

4. Long, J.S. 2008, The Workflow of Data Analysis Using Stata. Stata Press: College Station, TX. If you plan to do a lot of data analysis, this book will save you a lot of time and make your work replicable. Recommended but not required.

Getting ready for the class

1. Review a book on the linear regression model.

2. If you are rusty on mathematics, begin by reviewing these materials.

3. Familiarize yourself with Stata. To get started, I recommend the Stata channel on YouTube.