Reviving a psychometric measure: Classification and prediction of the Operant Motive Test

Dirk Johannßen, Chris Biemann, David Scheffer


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
Venues:
CLPsych | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
121–125
Language:
URL:
https://www.aclweb.org/anthology/W19-3014
DOI:
10.18653/v1/W19-3014
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3014.pdf