Indiana University Bloomington

Statistics S503
Statistical Methodsll: Categorical Data Analaysis

Contact: Scott Long (
Offered: Fall, Every Year
Capacity: 30
Sequence: Soc 554: Statistical Methods I - course on linear regression
Pre-Requisites: Soc 554 - linear regression or similar class.
Algebra Required: Notation only
Calculus Required: Concepts
Contact Person for Authorization: Graduate secretary in Statistics
Instructor: Scott Long
Days Per Week Offered: Two lectures a week; two computer labs per week
Syllabus: No Syllabus Avaliable
Keywords: regression models; categorical outcomes; logit; probit; count models
Description: Categorical Data Analysis deals with regression models in which the dependent variable is categorical: binary, nominal, ordinal, and count. Models that are discussed include probit and logit for binary outcomes, ordered logit and ordered probit for ordinal outcomes, multinomial logit for nominal outcomes, and Poisson regression and related models for count outcomes.
Books: Lecture notes; Long & Freese. 2014. Regression Models for Categorical Dependent Variables Using Stata, 3rd ddition. Recommended: Long. 1997. Regression Models for Categorical and Limited Dependent Variables.
Substantive Orientation: social sciences; non-experimental
Statistical Orientation: non-experimental; maximum likelihood
Applied/Theoretical: applied with explanations (not derivations) of statistical foundations.
Formal Computing Lab: Yes
Software: Stata
How Software is Used: data analysis, some programming
Problem Sets: Yes, with most involving analysis and interpretation of data.
Data Analysis: Yes as part of problem sets
Presentation: No
Exams: No
Comments: This is equivalent to Soc 650