On this page we present the work of our members around the topic of Personality Computing:
On this page we present the work of our members around the topic of Personality Computing:
Conceptual Work
Schoedel, R., Altgassen, E., Back, M. D., Buss, M., Bühner, M., Etzel, J. M., Goecke, B., Grinschgl, S., Grunenberg, E., Hätscher, O., Hommel, B. E., Kordsmeyer, T. L., Körner, S., Kuper, N., Mata, R., Mussel, P., Petrasch, A., Rammstedt, B., Rau, R., Short, C. A., Sindermann, C., Stachl, C., Stasielowicz, L., Sust, L., Thelen, J., Weiss, S., Wilhelm, O., Wulff, D., Wrzus, C., Ziegler, M. (2026). Künstliche Intelligenz und Individuelle Unterschiede. Eine Perspektive der Fachgruppe Differentielle Psychologie, Persönlichkeitspsychologie und Psychologische Diagnostik. Psychologische Rundschau, 77(3), 149-155. https://doi.org/10.1026/0033-3042/a000766. English preprint version: https://doi.org/10.31234/osf.io/j8szh_v2
Phan, L. V. & Rauthmann, J. F. (2021). Personality computing: New frontiers in personality assessment. Social and Personality Psychology Compass, 15(7). https://doi.org/10.1111/spc3.12624
Tutorials & Methodological Work
Schoedel, R., Sust, L., Sterner, P., Goretzko, D. (2026). From Digital Data to Psychological Insights: Making Sense of Mobile-Sensing Data through Integrative Preprocessing Pipelines. Psychometrika. Published online, 1-28. DOI 10.1017/psy.2026.10083
Pargent, F., Schoedel, R., Stachl, C. (2023). Best Practices in Supervised Machine Learning: A Tutorial for Psychologists. Advances in Methods and Practices in Psychological Science. 2023;6(3). https://doi.org/10.1177/25152459231162559
Empirical Work
Grunenberg, E., Klinz, J. L., Breil, S. M., Ahrens, H., & Back, M. D. (2026). Towards a better understanding of social judgments using machine learning: The case of performance judgments. European Journal of Personality, 40(2), 413-434.
Grunenberg, E., Stachl, C., Breil, S. M., Schäpers, P., & Back, M. D. (2025). Predicting and explaining assessment center judgments: A cross‐validated behavioral approach to performance judgments in interpersonal assessment center exercises. Human Resource Management, 64(2), 423-445.
Grunenberg, E., Peters, H., Francis, M. J., Back, M. D., & Matz, S. C. (2024). Machine learning in recruiting: Predicting personality from CVs and short text responses. Frontiers in Social Psychology, 1, Article 1290295. https://doi.org/10.3389/frsps.2023.1290295
Jankowsky, K., Krakau, L., Schroeders, U., Zwerenz, R., & Beutel, M. E. (2024). Predicting treatment response using machine learning: A registered report. British Journal of Clinical Psychology, 63(2), 137-155. https://doi.org/10.1111/bjc.12452
Jankowsky, K., Zimmermann, J., Jaeger, U., Mestel, R., & Schroeders, U. (2024). First impressions count: Therapists’ impression on patients’ motivation and helping alliance predicts psychotherapy dropout. Psychotherapy Research, 1-13. https://doi.org/10.1080/10503307.2024.2411985
Jankowsky, K., Steger, D., & Schroeders, U. (2023). Predicting lifetime suicide attempts in a community sample of adolescents using machine learning algorithms. Assessment, Advance online publication. https://doi.org/10.1177/10731911231167490
Jankowsky, K. & Schroeders, U. (2022). Validation and generalizability of machine learning prediction models on attrition in longitudinal studies. International Journal of Behavioral Development, 46(2), 169–176. https://doi.org/10.1177/01650254221075034
Reiter, T., & Schoedel, R. (2024). Never miss a beep: Using mobile sensing to investigate (non-) compliance in experience sampling studies. Behavior Research Methods, 56(4), 4038-4060. https://doi.org/10.3758/s13428-023-02252-9
Schoedel, R., Kunz, F., Bergmann, M., Bemmann, F., Bühner, M., & Sust, L. (2023). Snapshots of daily life: Situations investigated through the lens of smartphone sensing. Journal of Personality and Social Psychology. Advance online publication. https://doi.org/10.1037/pspp0000469
Schoedel, R., Au, Q., Völkel, S., Lehmann, F., Becker, D., Bühner, M., Bischl., B., Hussmann, H. & Stachl, C. (2018). Digital footprints of sensation seeking: a traditional concept in the big data era. In Zeitschrift für Psychologie, 226(4), 232-245. https://doi.org/10.1027/2151-2604/a000342
Sindermann, C., Mõttus, R., Rozgonjuk, D., & Montag, C. (2021). Predicting current voting intentions by Big Five personality domains, facets, and nuances – A random forest analysis approach in a German sample. Personality Science, 2(1). https://doi.org/10.5964/ps.6017
Sust, L., Stachl, C., Kudchadker, G., Bühner, M., & Schoedel, R. (2023). Personality Computing with Naturalistic Music Listening Behavior: Comparing Audio and Lyrics Preferences. Collabra: Psychology, 9(1). https://doi.org/10.1525/collabra.75214