REGRESSION MODELS FOR LIMITED DEPENDENT VARIABLES WITH STATA
Linear regression models are widely used in social sciences to analyze the statistical relationship between a dependent variable and a set of explanatory variables. This short course is an introduction to regression models for limited dependent variables --such as binary outcomes, ordinal categories, and nominal or non-ordered categories--, using Stata.
This course will cover regression models in a practical way using examples from Political Science, Mexican or Latin American politics. The course will begin with an introduction to maximum likelihood estimation methods, and then we will discuss three types of regression models: logit/probit models for binary outcomes; ordered logit/probit for ordinal outcomes; and multinomial logit for nominal outcomes.
This course will cover maximum likelihood models in a practical way, however, some familiarity with multiple linear regression (OLS), hypothesis testing, and Stata is recommended. Introductory statistical analysis with Stata or R can be taken jointly with this course.
Online classes according to Mexico city time
Date: August 29 to Sep 02, 2022
Schedule: 17:00 - 20:00 h