Teaching

Teaching

I primarily teach statistics, data analysis, and research methods in psychology at both the undergraduate and graduate level. Since 2011, I have taught 51 courses, 17 of which were formally evaluated (N = 210). Across these evaluations, students consistently rated my teaching highly, with an average instructor rating of 1.45 (N = 171, SD between classes = 0.22; German grading scale from 1 = very good to 6 = unsatisfactory). The overall course content received an average rating of 1.68 (N = 210, SD = 0.30).

In addition, I have supervised 22 thesis projects.

Research Methods (Bachelor)

Methods I

A major focus of my teaching has been introductory research methods (Methods I). Over the years, I have developed a comprehensive collection of teaching materials in German, which are published under a CC license and also available in print through KDP: https://amzn.eu/d/fxOVD7m.

The material contains 159 exercises covering topics such as:

  • Everyday psychology vs. scientific psychology
  • Philosophy of science
  • Measurement and testing (including conjoint measurement theory)
  • Observation and questionnaire methods
  • Experimental design and descriptive statistics
  • Correlation and regression
  • Probability theory
  • Introduction to R
  • Logic of significance testing
  • t-tests
  • Confidence intervals
  • ANOVA
  • Meta-analysis
  • Qualitative methods

The overall goal of these materials is to combine methodological rigor with practical relevance and extensive hands-on training.

Methods II

I also have extensive teaching experience in Methods II, which covers a broader and more advanced range of methodological topics. Current teaching materials are available exclusively to enrolled students, although I plan to develop them into a book in the future.

Topics include:

  • Contrast analysis
  • Dependent-samples methods (t-tests, ANOVA, contrast analysis)
  • Non-parametric statistics
  • Factor analysis
  • Cluster analysis
  • Non-linear relationships
  • Exploratory data analysis
  • Limitations of significance testing

Particular emphasis is placed on understanding the assumptions, strengths, and limitations of statistical methods rather than merely applying them mechanically.

R

For courses involving R, I use my dedicated learning platform: https://rlernen.de

I also co-authored the German-language textbook:

Burkhardt, Titz, & Sedlmeier — Datenanalyse mit R: Fortgeschrittene Verfahren. Pearson Studium Psychologie. ISBN-10: 3868944133 ISBN-13: 978-3868944136

The book focuses on advanced statistical procedures and includes a comprehensive chapter on multilevel modeling. It is available through major bookstores and directly from Pearson: https://www.pearson.de/datenanalyse-mit-r-fortgeschrittene-verfahren-9783868944136

In addition, I recommend the Essential R Cheatsheets, which include a compact Basic Statistics Cheatsheet covering many core aspects of applied data analysis: https://amzn.eu/d/b3aUNdO

Research Methods (Master)

At the graduate level, I have taught seminars, exercises, and lecture sessions on topics such as:

  • Computer simulation
    • Artificial neural networks
    • Cellular automata
  • Mixed and multilevel models
  • Formal modeling in psychology

I also co-taught a specialized seminar on artificial neural networks together with Prof. Dr. Peter Sedlmeier.

YouTube

I maintain a YouTube channel dedicated to research methods, statistics, and R programming: https://youtube.com/@methodenmonster

The content is a mixture of English and German, although most videos are currently in German.

Teaching Approach

My teaching philosophy emphasizes learning through authentic scientific problems. Many exercises and examples are therefore based on real research questions, real datasets, and published scientific papers rather than artificial toy examples.

I believe this approach substantially improves methodological understanding because students learn statistical methods in the context in which they are actually used: scientific reasoning, data interpretation, and critical evaluation of empirical evidence.

This perspective is partly inspired by Klaus Grawe’s concept of problem actuation in psychotherapy research. The central idea is that meaningful learning often requires engaging with problems in a realistic and personally relevant context. In the same way that psychological interventions become more effective when underlying problems are actively experienced, methodological competence develops most effectively when students work through authentic scientific challenges.

As a consequence, my courses frequently integrate original research articles and realistic analytical tasks. Students are encouraged not only to apply statistical techniques, but also to understand why a particular method is appropriate, which assumptions it relies on, and how methodological decisions influence scientific conclusions.

Additional Teaching Topics

Beyond the courses listed above, I also offer expertise in the following areas:

  1. Mixed models (HLM / multilevel modeling)
  2. Advanced R programming
    • Object-oriented programming (OOP)
    • Functional programming
    • Developing and publishing R packages on CRAN
    • Preparing reproducible analyses for journals
    • Efficient data analysis workflows using tidyverse tools
    • High-quality visualization with ggplot2
  3. Programming psychological experiments
    • PsychoPy (including eye-tracking experiments)
    • jsPsych for online experiments
  4. State-Trace Analysis
    • Experimental design
    • Statistical analysis
    • Interpretation and methodological applications