Abstract
Implicit motives allow for the characterization of behavior, subsequent success and long-term development. While this has been operationalized in the operant motive test, research on motives has declined mainly due to labor-intensive and costly human annotation. In this study, we analyze over 200,000 labeled data items from 40,000 participants and utilize them for engineering features for training a logistic model tree machine learning model. It captures manually assigned motives well with an F-score of 80%, coming close to the pairwise annotator intraclass correlation coefficient of r = .85. In addition, we found a significant correlation of r = .2 between subsequent academic success and data automatically labeled with our model in an extrinsic evaluation.- Anthology ID:
- W19-3014
- Volume:
- Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Kate Niederhoffer, Kristy Hollingshead, Philip Resnik, Rebecca Resnik, Kate Loveys
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 121–125
- Language:
- URL:
- https://aclanthology.org/W19-3014
- DOI:
- 10.18653/v1/W19-3014
- Cite (ACL):
- Dirk Johannßen, Chris Biemann, and David Scheffer. 2019. Reviving a psychometric measure: Classification and prediction of the Operant Motive Test. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pages 121–125, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Reviving a psychometric measure: Classification and prediction of the Operant Motive Test (Johannßen et al., CLPsych 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W19-3014.pdf