September 20, 2021
Abstract
This paper examines a new personality assessment scoring approach labeled supervised forced choice scoring (SFCS), which aims to maximize construct validity of forced choice (FC) personality assessments. SFCS maximally weights FC responses to predict or “reproduce” honest, normative, and reliable personality scores using machine learning. In this proof of concept study, a graded response FC assessment was tested across several samples, and SFCS resulted in psychometric improvements over traditional FC scoring. Correlations with aligned single-stimulus trait scores (taken honestly) were strong, both when the FC measure was taken honestly and when taken in induced applicant settings. SFCS scores also exhibited small shifts in average scores between honest and faked conditions and were predictive of organizational citizenship behaviors, employee engagement, and leadership emergence at work. Although SFCS showed merit in this proof of concept study, it is unclear how well results will generalize to new FC measures, and we urge more research on this scoring method.
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Author: Andrew B. Speer,
Angie Y. Delacruz
