Predicting Leadership Status Through Trait Emotional Intelligence and Cognitive Ability.

Area of Science:

• Organizational Psychology
• Leadership Studies
• Data Science

Background:

• Managerial role prediction involves demographics, cognitive ability, and trait emotional intelligence (trait EI).
• Traditional linear models may not capture the complexity of leadership prediction.
• Supervised machine learning (SML) offers advanced methods for analyzing complex relationships.

Purpose of the Study:

• To compare the predictive power of trait EI and cognitive ability for managerial roles.
• To evaluate the effectiveness of SML algorithms against traditional linear approaches.
• To identify key factors and interactions influencing leadership positions using SML.

Main Methods:

• Logistic regression as a traditional linear baseline.
• Application of supervised machine learning (SML) algorithms.
• Utilized feature importance and interaction analysis within SML.

Main Results:

• Trait EI demonstrated superior predictive strength compared to cognitive ability.
• The sociability factor of trait EI was particularly influential.
• The random forest (RF) algorithm showed significant predictive utility.

Conclusions:

• SML approaches, like random forest, are valuable for understanding leadership complexity.
• Trait EI, especially sociability, is a critical predictor of managerial roles.
• Future research should explore more sophisticated SML methods for leadership studies.