After obtaining my PhD from LMU Munich and substituting two professorships (Computational Modeling in Psychology at LMU Munich & Psychological Methods at Leipzig University), I am now an Assistant Professor at the Department of Methodology and Statistics. My research primarily focuses on the integration of modern data science/machine learning methods and psychometrics.
Goretzko, D., & Bühner, M. (2020). One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis. Psychological Methods, 25(6), 776–786. https://doi.org/10.1037/met0000262.
Goretzko, D., Heumann, C., & Bühner, M. (2020). Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data Methods. Educational and Psychological Measurement, 80(4), 756–774. https://doi.org/10.1177/0013164419893413
Goretzko, D., & Israel, L. S. F. (2021). Pitfalls of Machine Learning based Personnel Selection – Fairness, Transparency and Data Quality. Journal of Personnel Psychology. https://econtent.hogrefe.com/doi/full/10.1027/1866-5888/a000287
Goretzko, D. (2022). Factor Retention in Exploratory Factor Analysis With Missing Data. Educational and Psychological Measurement, 82(3), 444–464. https://doi.org/10.1177/00131644211022031
Goretzko, D., & Bühner, M. (2022). Factor Retention Using Machine Learning With Ordinal Data. Applied Psychological Measurement, 46(5), 406–421. https://doi.org/10.1177/01466216221089345
Sterner, P., & Goretzko, D. (2023) Exploratory Factor Analysis Trees: Evaluating Measurement Invariance Between Multiple Covariates, Structural Equation Modeling: A Multidisciplinary Journal, DOI: 10.1080/10705511.2023.2188573
Goretzko, D., & Ruscio, J. (2023). The Comparison Data Forest – A new comparison data approach to determine the number of factors in exploratory factor analysis. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02122-4