I specialize in teaching courses focused on quantitative methods and research design to social science students. My teaching portfolio spans several years and includes experience at multiple institutions and in diverse educational formats. In addition, I have supervised numerous Bachelor’s and Master’s theses, providing comprehensive academic guidance and support. The student evaluations showcased below reflect the consistent positive reception of my courses.

I have provided a curated list of courses, accompanied by a brief overview of my teaching experience.

Open-access Courses

Introduction to R

If you’re interested in mastering the fundamentals of R, I’ve created an hands-on Colab designed to facilitate an intuitive understanding of R’s core logic and essential functions. This exercise offers a practical approach to learning, allowing you to apply concepts as you go. To get started and explore these foundational aspects of R programming, access the exercise here.

Topic Modelling

My workshop Topic Modelling in R and Python, offered as part of the Workshops for Ukraine series, is now openly accessible. The slides of this workshop are available here. For a hands-on experience, you are invited to explore the course materials through interactive Colab notebooks: the first Colab focuses on R implementations, the second Colab delves into Python applications. These resources are designed to enhance your learning and application of various topic modeling techniques in both languages, using real world newspaper texts as examples.

Multilevel Modelling

My comprehensive research training Comparative Social Research with Multi-level Modelling in R is available in open access. The aim of the course is to equip students with the skills to conduct their own comparative studies in R. The curriculum is structured around data from my favorite large-scale survey program, the European Social Survey. This data provides students with real-world insights and a practical understanding of multilevel modeling. The research training consists of 15 sessions, each including a 90-minute lecture followed by a 90-minute exercise. This ensures a balanced blend of theoretical knowledge and practical application. The course’s in-depth content and hands-on approach make it an ideal resource for researchers delving into comparative social research and multilevel modeling using R.

I gave this training originally at Goethe University Frankfurt during the winter term of 2021/22. Prior to this, I regularly taught this course in Stata, from which it was translated to R. If you are interested in the materials using Stata, write me an email.

Past Courses and Evaluations

• Oct 2023: Workshops for Ukraine: Introduction to Topic Modelling in R and Python
• Aug 2023: DeZIM Summer School: Einführung in die Panelregression
• Jul 2023: BIGSSS Summer School on Computational Social Science of Democratic Debate: Topic Models
• Summer 2022: Längsschnittdatenanalyse in R
• Winter 2021/22: Vergleichende Sozialforschung mit Mehrebenenmodellen in R
• Aug 2021: Frankfurt Digital Summer School: Multilevel Analysis
• Summer 2021: Lägsschnittdatenanalyse und Kausalität
• Winter 2020/21: Längsschnittdatenanalyse und Kausalität

• Summer 2020: Längsschnittdatenanalyse und Kausalität
• Winter 2019/20: Vergleichende Sozialforschung mit Mehrebenenmodellen
• Summer 2019: Analyzing longitudinal data and the issue of causality
• Winter 2018/19: Quantitative comparative social research with multi-level modeling
• Summer 2018: An applied introduction into quantitative comparative social research
• Winter 2017/18: Analysis of cross-sectional data
• Summer 2017: Analysis of longitudinal data
• Winter 2016/17: Analysis of cross-sectional data
• Summer 2016: Analysis of longitudinal data