Using Mobile Sensors to Study Personality Dynamics

Brenton M. Wiernik, Deniz S. Ones, Benjamin M. Marlin, Casey Giordano, Stephan Dilchert, Brittany K. Mercado, Kevin C. Stanek, Adib Birkland, Yilei Wang, Brenda Ellis, Yagizhan Yazar, Jack W. Kostal, Santosh Kumar, Timothy Hnat, Emre Ertin, Akane Sano, Deepak K. Ganesan, Tanzeem Choudhoury, Mustafa Al'Absi

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)935-947
Number of pages13
JournalEuropean Journal of Psychological Assessment
Volume36
Issue number6
DOIs
StatePublished - Nov 2020

Keywords

  • machine learning
  • mobile sensing
  • personality
  • personality dynamics
  • states

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