Data-driven vs. psychological personality temperaments: theoretical and clinical utility of personality measures in psychiatry - Sawalma, Abdelrahman S.; Sehwail, Mahmud A.; Dammers, Jürgen; Herzallah, Mohammad M. (Frontiers in Psychiatry, 2024)
Decades of research on personality identified dissociable psychological temperaments. Cloninger's temperament and character theory used a psychobiological approach to differentiate three major dimensions of personality: harm avoidance, novelty seeking, and reward dependence. Previous studies, heretofore, did not examine the correspondence between Cloninger's psychological temperaments and statistically independent data-driven components and how that could enhance the clinical utility of personality temperaments. In this study, we validated an Arabic version of the tri-dimensional personality questionnaire (TPQ) to construct data-driven personality temperaments using independent component analysis (ICA). Using SVM, we contrasted the clinical utility of data-driven personality vs. Cloninger's psychological temperaments in differentiating medication-naïve patients with major depressive disorder (N=244) and healthy subjects (N=1109). Data-driven personality components based on ICA showed very little overlap with Cloninger's original temperaments. Both Cloninger's temperaments and data-driven components revealed low internal consistency (for subscales) but high test-retest reliability. Cloninger's temperaments, however, showed a poor goodness-of-fit for the structure of the TPQ. Data-driven components significantly outperformed psychological TPQ temperaments with higher accuracy and recall but not precision. To our knowledge, this is the first study to examine the clinical utility of data-driven vs. psychological personality metrics using a sizeable sample of patients and healthy individuals. Our results could have wide implications for reexamining psychometric data to extract data-driven latent structures that can improve replicability, clinical utility, and cross-disciplinary inference.