Contact

Julian Mollenhauer, M.Sc.
Department of Psychology
Research Methods and Mathematical Psychology
Schleichstrasse 4, 72076 Tuebingen, Germany
julian.mollenhauer[at]uni-tuebingen.de

Interests

  • Knowledge space theory
  • Simulation based methods
  • Clinical psychology
  • R / Shiny

Education

Oct 2021 M.Sc. Psychology, University of Tübingen.
Mollenhauer, J (2021). Essverhalten im Verlauf eines Geschmackstests bei Menschen mit einer Binge-Eating-Störung oder Übergewicht. [Eating behaviour over the course of a bogus taste test in people with binge-eating disorder or obesity.] Master's thesis, University of Tübingen, Germany. [pdf]
Mar 2021 Programming Shiny apps. Workshop at the Erasmus+ Project: Higher Education Learning Platform for Quantitative Thinking [https://qhelp.eu/]. Online seminar.
Shiny App: A Probability Model for Golf Putting. See QHELP
Oct 2020 B.Sc. Psychology, University of Tübingen.
Mollenhauer, J (2020). Fehlende Daten in probabilistischen Wissensstrukturen. Eine Simulationsstudie. [Missing data in probabilistic knowledge structures. A simulation study.] Bachelor's thesis, University of Tübingen, Germany. [pdf]
Mar 2019 Programming Shiny apps. Workshop at the Erasmus+ Project: Tools for Teaching Quantitative Thinking [https://tquant.eu/]. Balatonföldvär, Hungary.
Shiny App: Power under certain violations of test assumptions. See TQUANT

Software

  • R package "pksCpp" based on pks
    Provides additional functions mostly written in C++ to estimate probabilistic knowledge space models.
    Dev. version: see GitHub
  • Cognitive Modeling [materials and shiny visualisations]
  • Bogus Taste Test:
    Shiny app to clean longitudinal weight data from a bogus taste test. The app identifies artifacts in the weight curve, removes them and interpolates the data. Each data series can be visually inspected and the cleanup parameters can be adjusted individually if necessary. See GitHub

Teaching

2022/2023
  • Seminar: Computergestützte Statistik [Computer-Assisted Statistics]
2022
  • Seminar: Computergestützte Statistik [Computer-Assisted Statistics]
2021/2022
  • Tutorial: Advanced Statistics [Vorlesung Statistik III]
2021
  • Tutorial: Computergestützte Methoden [Computer-Assisted Research Methods]
2020/2021
  • Tutorial: Forschungsmethoden der Psychologie [Research methods in psychology]
2020
  • Tutorial: Computergestützte Methoden [Computer-Assisted Research Methods]
2019
  • Tutorial: Computergestützte Methoden [Computer-Assisted Research Methods]
2018/2019
  • Tutorial: Forschungsmethoden der Psychologie [Research methods in psychology]