Zadorozhny Bogdan S, Petrides K V, Cheng Yongtian, Cuppello Stephen, van der Linden Dimitri
Department of Psychology, University College London (UCL), London WC1E 6BT, UK.
Thomas International, Marlow SL7 2NL, UK.
Behav Sci (Basel). 2025 Mar 11;15(3):345. doi: 10.3390/bs15030345.
Many interconnected factors have been implicated in the prediction of whether a given individual occupies a managerial role. These include an assortment of demographic variables such as age and gender as well as trait emotional intelligence (trait EI) and cognitive ability. In order to disentangle their respective effects on formal leadership position, the present study compares a traditional linear approach in the form of a logistic regression with the results of a set of supervised machine learning (SML) algorithms. In addition to merely extending beyond linear effects, a series of techniques were incorporated so as to practically apply ML approaches and interpret their results, including feature importance and interactions. The results demonstrated the superior predictive strength of trait EI over cognitive ability, especially of its sociability factor, and supported the predictive utility of the random forest (RF) algorithm in this context. We thereby hope to contribute and support a developing trend of acknowledging the genuine complexity of real-world contexts such as leadership and provide direction for future investigations, including more sophisticated ML approaches.
许多相互关联的因素与预测某个人是否担任管理角色有关。这些因素包括各种人口统计学变量，如年龄和性别，以及特质情商（特质EI）和认知能力。为了厘清它们对正式领导职位的各自影响，本研究将逻辑回归形式的传统线性方法与一组监督机器学习（SML）算法的结果进行了比较。除了仅仅超越线性效应之外，还纳入了一系列技术，以便实际应用机器学习方法并解释其结果，包括特征重要性和相互作用。结果表明，特质EI比认知能力具有更强的预测力，尤其是其社交能力因素，并支持随机森林（RF）算法在此背景下的预测效用。因此，我们希望做出贡献并支持一种发展趋势，即承认领导力等现实世界背景的真正复杂性，并为未来的研究提供方向，包括更复杂的机器学习方法。
