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André Ernst
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Janina Götsche

Logistic Regression and Beyond: Modelling Categorical Dependent Variables

Cologne / Unter Sachsenhausen 6-8
General Topics:
Course Level:
Software used:
Students: 300 €
Academics: 450 €
Commercial: 900 €
Additional links
Lecturer(s): Sophie Suda

About the lecturer - Sophie Suda

Course description

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.

Target group

Participants will find the course useful if:
  • they have basic knowledge of linear regression and Stata
  • they intend to use or already use categorical independent variables in their work/research

  • Learning objectives

    By the end of the course participants will acquire competencies in the following domains with regard to regression modeling:
  • knowledge and understanding of key concepts of regression models with binary, ordinal, or nominal dependent variables
  • skills and abilities to apply regression models for such categorical independent variables
  • ability to evaluate and approach these models
    Organizational structure of the course
    The course consists of several modes of teaching and learning
  • Lectures
  • Hands-on sessions
  • Independent work on a research project in small groups
    In the lectures, participants will learn basic statistical concepts of regression models with categorical dependent variables. Examples, quizzes, and exercises in the lectures will support participants' understanding of the concepts. Questions and discussing the concepts during the lectures are highly appreciated.
    Hands-on session
    Participants will apply the statistical methodology introduced in the lecture in hands-on lab sessions. The lecturer will be available to support work on the exercises and assignments.
    Independent work on research project
    In addition to the lecture and pre-structured hand-on session participants work independently with their own data or Eurobarometer data in small groups of two of three. This group work will be guided by specific tasks and assignments and supported individually by the lecturer. At the end of the workshop the groups will present and discuss their analyses and results.


    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.
    Software and hardware requirements
    For this workshop, you need a laptop and the statistical software Stata. If you can't bring a laptop or need Stata, please let us know the latest two weeks in advance of the course start so that we can take care of it. Please note that participation without Stata is not possible.
    Monday, 17.07.
  • Lecture: introduction, recap (binary variables, distributions odds and probabilities)
  • Lecture: linear probability models, logit transformation, logistic regression I
  • Hands-on session: odds, linear probability models and logistic regression using Stata
  • 12:30-13:30Lunch Break
  • Lecture: logistic regression II, interpretation
  • Hands-on session: logistic regression using Stata, interpretation
  • Hands-on session: predicted values
  • Group exercise: research project
  • Tuesday, 18.07.
  • Lecture: recap, model fit, hypothesis testing
  • Hands-on session: testing models and hypotheses
  • Group work: testing yesterday's models
  • 12:00-13:00Lunch Break
  • Lecture: interactions in logistic regression
  • Hands-on session: interactions
  • Group work: interactions
  • Lecture: probit and other link functions
  • Hands-on session: probit models
  • Wednesday, 19.07.
  • Lecture: ordered logistic and multinomial logistic regressions (assumptions, models, interpretation)
  • Hands-on session: ologit and mlogit
  • 12:00-13:00Lunch Break
  • Group work: ologit + mlogit
  • Hands-on session: margins and other quantities of interest
  • Group work: how to present your research using margins and more

  • Recommended readings