Wiernik, B. M., Ones, D. S., Marlin, B. M., Giordano, C., Dilchert, S., Mercado, B. K., Stanek, K. C., Birkland, A., Wang, Y., Ellis, B., Yazar, Y., Kostal, J. W., Kumar, S., Hnat, T., Ertin, E., Sano, A., Ganesan, D. K., Choudhoury, T., & al’Absi, M.
European Journal of Psychological Assessment, 36(6), 935–947.
Research interest in personality dynamics over time is rapidly growing. Passive personality assessment via mobile sensors offers an intriguing new approach for measuring a wide variety of personality dynamics. In this paper, we address the possibility of integrating sensorbased assessments to enhance personality dynamics research. We consider a variety of research designs that can incorporate sensor-based measures and address pitfalls and limitations in terms of psychometrics and practical implementation. We also consider analytic challenges related to data quality and model evaluation that researchers must address when applying machine learning methods to translate sensor data into composite personality assessments.