Are women more likely to attain a higher education degree than men? How does party preference vary across different social groups? Are Europeans concerned about climate change? In the social sciences, one often deals with categorical dependent variables. These can be variables whose characteristics are dichotomous (e.g., attaining a degree: yes/no), nominal (party preference for CDU, SPD, FDP, or Greens), or ordinal (no worries, some worries, big worries). In this workshop, we will discuss regression models for analyzing such categorical variables. Topics include linear probability models, logistic regression models, probit and other link functions, goodness-of-fit assessment, and an introduction to ordinal logistic regression and multinomial regression models as well as presentation of results for reports and publications. The statistical concepts are introduced, applied, and deepened through hands-on sessions using the statistical software Stata. In addition, participants will conduct small research projects in which they will independently analyze their own data or Eurobarometer data with categorical outcome variables in groups of two or three.

By the end of the course participants will acquire competencies in the following domains with regard to regression modeling:

 

 

The workshop assumes profound knowledge of univariate and bivariate statistics and inferential statistics (e.g., hypothesis testing), and basic knowledge of linear regression models. In addition, basic knowledge of the statistical software Stata is required for the hands-on session, i.e., participants should be able to open data sets in Stata using the syntax (do-files) and carry out simple descriptive analysis using standard commands.