Teaching

Sommersemester 2022

Forschungspraktikum I und II: Längsschnittdatenanalyse in R (4 hours per week): Course material is available on GitHub

Wintersemester 2021/22

Forschungspraktikum I und II: Vergleichende Sozialforschung mit Mehrebenenmodellen in R (4 hours per week): Course material is available on GitHub

Experience

I started teaching as a tutor for econometric lectures beginning in summer 2016 at the University of Cologne, while working on my PhD. Two years later, I changed to a full time position at Goethe University Frankfurt, where I am teaching courses on applying quantitative methods for social research. I’ve also given a course on multilevel modelling at the Frankfurt Summer School, which was open for PhD students. I teach in English and German, online and in-person, from BA to PhD.

I’m happy to share that students’ evaluation of my courses have, by and large, been very positive.

Past courses and evaluations

Wordcloud based on students' evaluations of my courses
  • SS 2022: Längsschnittdatenanalyse in R (4 h): 1.5/6
  • WS 2021/22: Vergleichende Sozialforschung mit Mehrebenenmodellen in R (4 h): 1.8/6
  • Aug 2021: Frankfurt Digital Summer School: Multilevel Analysis
  • SS 2021: Laegsschnittdatenanalyse und Kausalitaet (digital): 1.2/6
  • WS 2020/21: Laengsschnittdatenanalyse und Kausalitaet (digital): 1.2/6
  • SS 2020: Laengsschnittdatenanalyse und Kausalitaet (digital): 1.4/6
  • WS 2019/20: Vergleichende Sozialforschung mit Mehrebenenmodellen (4 h): 1.5/6
  • SS 2019: Analyzing longitudinal data and the issue of causality (4 h): 1.4/6
  • WS 2018/19: Quantitative comparative social research with multi-level modeling (4 h): 1.6/6
  • SS 2018: An applied introduction into quantitative comparative social research (block): 1.7/5 (unofficial)
  • WS 2017/18: Analysis of cross-sectional data (tutor): 1.4/5
  • SS 2017: Analysis of longitudinal data (tutor). No evaluation
  • WS 2016/17: Analysis of cross-sectional data (tutor): 1.6/5
  • SS 2016: Analysis of longitudinal data (tutor): 2.0/5

For all evaluations 1.0 = best