Dr. Philipp Sterner
Reserach Associate (data management) | TUM
Dr. Philipp Sterner has been a postdoctoral researcher at the Centre for International Student Assessment since September 2025 and is responsible for the scaling in the data management of the PISA-study. His research focuses on the development of methods to investigate measurement invariance, specifically using methodology from machine learning and causal inference.
Vita
- Researcher (Post-Doc) and data manager (scaling), Technical University of Munich, Programme for International Student Assessment (PISA; since 09/2025)
- Researcher (Post-Doc), Ruhr-University Bochum and German Center for Mental Health (DZPG; 01/2024 – 08/2025)
- Researcher at Ludwig-Maximilians-Universität of Munich, Department of Psychology (10/2021 – 10/2024)
- Doctoral degree (Dr. phil.) in Psychology, Ludwig-Maximilians-Universität in Munich (title of dissertation: “On the investigation of measurement invariance: New developments and a causal framework for future research”, 02/2025).
- Master's degree in Statistics and Data Science, Ludwig-Maximilians-Universität of Munich (04/2022 – 09/2025)
- Master's degree in Psychology, Ludwig-Maximilians-Universität of Munich (10/2018 – 03/2021)
- Bachelor's degree in Psychology, Ludwig-Maximilians-Universität of Munich (10/2014 – 12/2017)
Research Interests
- Measurement invariance
- (Cost-sensitive) machine learning
- Causal inference
- Structural equation modeling
Activities
- PISA 2025 data management (scaling)
- PISA 2025 methods chapter
Selected publications
- Sterner, P., & Goretzko, D. (2023). Exploratory Factor Analysis Trees: Evaluating Measurement Invariance Between Multiple Covariates. Structural Equation Modeling: A Multidisciplinary Journal, 30(6), 871-886. https://doi.org/10.1080/10705511.2023.2188573
- Sterner, P., De Roover, K., & Goretzko, D. (2024). New Developments in Measurement Invariance Testing-An Overview and Comparison of EFA-based Approaches. Structural Equation Modeling: A Multidisciplinary Journal, 32(1), 117-135. https://doi.org/10.1080/10705511.2024.2393647
- Sterner, P., Pargent, F., Deffner, D., & Goretzko, D. (2024). A Causal Framework for the Comparability of Latent Variables. Structural Equation Modeling: A Multidisciplinary Journal, 31(5), 747-758. https://doi.org/10.1080/10705511.2024.2339396
- Sterner, P., Goretzko, D., & Pargent, F. (2025). Everything has its price: Foundations of costsensitive machine learning and its application in psychology. Psychological Methods, 30(1), 112-127. https://doi.org/10.1037/met0000586
- Sterner, P., Friemelt, B., Goretzko, D., Kraus, E. B., Bühner, M., & Pargent, F. (2024). Das Konfidenz-/Signifikanzniveau impliziert ein bestimmtes Kostenverhältnis zwischen Fehler 1. Art und Fehler 2. Art: Für ein stärkeres Einbeziehen der Entscheidungstheorie in die psychologische Einzelfalldiagnostik. Diagnostica, 70(3), 126–138. https://doi.org/10.1026/0012-1924/a000329
- Goretzko D., Partsch, M. V., & Sterner, P. (2025). Embrace the heterogeneity in EFA but be transparent about what you do – A commentary on Manapat et al. (2023). Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000759
- Goretzko, D., & Sterner, P. (2025). Exploratory Graph Analysis Trees - A Network-based Approach to Investigate Measurement Invariance with Numerous Covariates. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000796