Susanne Frick
Contact
TU Dortmund University
Department of Statistics
Chair of Statistical Methods in Social Sciences
Mathematics, Room 732
44221 Dortmund
Germany
E-Mail: frick@statistik.tu-dortmund.de
Phone: 0231 755 8327

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Department of Statistics
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TU Dortmund University
Department of Statistics
Chair of Statistical Methods in Social Sciences
Mathematics, Room 732
44221 Dortmund
Germany
E-Mail: frick@statistik.tu-dortmund.de
Phone: 0231 755 8327

Susanne Frick is a postdoctoral researcher at the chair of Statistical Methods in Social Sciences at TU Dortmund University and worked from 2022 to 2024 in the interdisciplinary research project FAIR (https://fair.tu-dortmund.de) since February 2022. She earned a PhD in psychology at the University of Mannheim in the research training group Statistical Modeling in Psychology (SMiP) in December 2021. From 2013 to 2018, she studied psychology at the University of Constance. Her research interests include psychometrics, especially the modelling of forced-choice and process data, as well as machine learning for the prediction of interventions and for successful replications.
Frick, S., Schmid, L., Kuhn, J.-T., Pauly, M., & Doebler, P. (2025). Using Multivariate Random Forests for Predicting Learning Trajectories From Digital Training Data. Accpeted for publication in Quantitative and Computational Methods in the Behavioral Sciences.
Heister, H., Doebler, P., & Frick, S. (2025). Bayesian Thurstonian IRT Modeling: Logical Dependencies as an Accurate Reflection of Thurstone’s Law of Comparative Judgment. Educational and Psychological Measurement, 00131644251335586. https://doi.org/10.1177/00131644251335586
Doebler*, P., Frick*, S., & Doebler, A. (2024). Beta-Binomial Meta-Analysis of Individual Differences Based on Sample Means and Standard Deviations: Studying Reliability of Sum Scores of Binary Items. Psychological Methods, Advance online publication. https://doi.org/10.1037/met0000649
Frick**, S., Krivošija**, A., & Munteanu**, A. (2024). Scalable Learning of Item Response Theory Models from Large Data. Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:1234–1242. Retrieved from https://proceedings.mlr.press/v238/frick24a.htm
Frick, S. (2023). Estimating and Using Block Information in the Thurstonian IRT Model. Psychometrika, 88(4), 1556–1589. https://doi.org/10.1007/s11336-023-09931-8
Frick, S. (2022). Modeling Faking in the Multidimensional Forced-Choice Format - The Faking Mixture Model. Psychometrika, 87, 773–794. https://doi.org/10.1007/s11336-021-09818-6
Frick, S., Brown, A., & Wetzel, E. (2023). Investigating the normativity of trait estimates from multidimensional forced-choice data. Multivariate Behavioral Research, 58(1), 1–29. https://doi.org/10.1080/00273171.2021.1938960
Wetzel, E., Frick, S., & Brown, A. (2021). Does multidimensional forced-choice prevent faking? Comparing the susceptibility of the multidimensional forced-choice format and the rating scale format to faking. Psychological Assessment, 33(2), 156–170. https://doi.org/10.1037/pas0000971
* = shared first authorship
** = alphabetical order of authors