Exploring emotional stability: from conventional approaches to machine learning insights: Exploring emotional stability: from conventional approaches to machine learning insights

In contemporary psychological assessments, diverse traits are often evaluated using extensive questionnaires. This study focuses on the trait of emotional stability, and acknowledges the inherent limitations and issues associated with prolonged survey instruments. To address these challenges, we pro...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2025-02, Vol.55 (3), p.213, Article 213
Main Authors: Madroñal, Marcos Romero, Ramírez, Eduar S., Ruiz, Luis Gonzaga Baca, Serrano-Fernández, María José, Pérez-Moreiras, Elena, Pegalajar Jiménez, María del Carmen
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Machine learning
Machines
Manufacturing
Mechanical Engineering
Processes
Psychological factors
Questionnaires
Stability
Citations: Items that this one cites
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Summary:In contemporary psychological assessments, diverse traits are often evaluated using extensive questionnaires. This study focuses on the trait of emotional stability, and acknowledges the inherent limitations and issues associated with prolonged survey instruments. To address these challenges, we propose a Machine Learning (ML) approach to directly predict emotional stability, offering a more efficient alternative to bulky questionnaires. The study carefully selected variables with previously established relationships to emotional stability, utilizing a dataset of 2203 individuals who responded to a series of psychometric questionnaires. The proposed method yields promising results, achieving an R2 score of approximately 0.71 on the test set, indicating robust predictive performance. These models highlighted the significance of variables such as emotional stress and self-esteem, emphasizing their substantial role in predicting emotional stability. It is noteworthy that even with a reduced set of variables, the models remained statistically equivalent. The results provide valuable insights for predicting stability with smaller sets of variables and contribute knowledge that complements the understanding of emotional stability.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-024-06130-5