Maximum Likelihood Estimation I: Generalized Linear Models


  • Robert Lupton, University of Connecticut

This workshop will be offered in an online video format.

This course introduces participants to a variety of statistical models that are used when assumptions of ordinary linear regression are violated. Participants will learn to estimate, interpret and present logistic and probit regression models for use with binary, ordinal and nominal dependent variables, as well as models for event count data. Maximum likelihood unifies these models by providing a single, coherent approach to estimation and thinking about the data generating process. Participants will learn the logic underlying these models, although the course’s emphasis will be applying these methods to substantive social science research questions.

Background: The mathematical background needed for the course is multiple regression using linear algebra. Attendance at the Mathematics for Social Scientists, II lectures should prove useful.

Fee and Registration: This course is part of the first four-week session. Please see our fee chart on our Registration page for the cost of attending one (or both) four-week session(s). Participants who enroll in a four-week session may take as many courses (workshops and lectures) as desired during the session for which they are enrolled. Participants in the four-week sessions are also welcome to attend all of the lectures and discussions offered in the Blalock Lecture Series.

Tags: logit, probit, nonlinear

Course Sections

Section 1

Location: Online -- Video format,

Date(s): June 22 - July 17

Time: 1:00 PM - 3:00 PM


  • Robert Lupton, University of Connecticut