10.17605/OSF.IO/WVDJT
Antonis Koutsoumpis
Antonis
Koutsoumpis
https://osf.io/ae3wn/
0000-0001-9242-4959
Janneke Oostrom
Janneke
Oostrom
https://osf.io/59pcs/
Sina Ghassemi
Sina
Ghassemi
https://osf.io/a64wq/
Djurre Holtrop
Djurre
Holtrop
https://osf.io/snm9d/
0000-0003-3824-3385
Reinout E. de Vries
Reinout
de Vries
https://osf.io/tcgxk/
0000-0002-4252-5839
Vrije Universiteit Amsterdam
Vrije Universiteit Amsterdam
Vrije Universiteit Amsterdam
Ward van Breda
Ward
van Breda
https://osf.io/qkwpr/
Automatic personality assessment from high-stakes two-way online job interviews - The LTP project
Open Science Framework
2022
Social and Behavioral Sciences
Personality
Open Science Framework
https://ror.org/05d5mza29/
https://grid.ac/institutes/grid.466501.0/
2021-11-26
2021-11-26
2022-08-30
Pre-registration
CC0 1.0 Universal
Real assessment candidates will take a high-stakes digital unstructured interview with a professional consultant at the LTP Business Psychology company, located in Amstelveen, the Netherlands. The video interview will take place online and will be video and audio recorded. As part of their assessment, applicants provide personality self-reports and demographic information. At a later stage, trained observers will watch the recordings and rate personality traits, competences, and the attractiveness of the candidates. After data collection, machine learning techniques will be applied to test whether self- and observer-reported personality scores can be predicted from video interviews. Three separate modalities of the video data will be extracted and tested: verbal features (the words individuals use), non-verbal features (facial expressions), and para-verbal features (voice characteristics). The main goal of the project is to develop a tool that automatically assesses personality from video interviews, and test whether it significantly explains self- and observer reported personality variance.