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
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
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., 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