Interpreting Binary Logistic Regression Models

This course will provide participants with the detailed understanding and advanced skills needed to interpret the results of logistic regression models with binary outcome variables.
Logistic regression models are very common in the social sciences, but their interpretation is different than for OLS regression models. To this end, this course will help participants to interpret the results of binary logistic regression models using log odds, odds ratios and above all predicted probabilities. A particular focus of this course will be how to present results in tables and graphs. This will help participants make the most out of their results, both for academic papers and for making their research accessible to lay readers. Where possible, reference will also be made to related statistical models that use multinomial and ordinal outcome variables.
The software package Stata will be used as this provides a user-friendly way of interpreting logistic regression results, particularly due to the useful add-ons SPost and Clarify.

Course leader

Markus Wagner University of Vienna

Target group

Advanced students and junior researchers in political science and adjacent disciplines

Course aim

as above