Introduction: The relevant scientific literature has confirmed the relationship between emotional intelligence (EI) and mental health. In addition, previous studies have underlined the importance of perceived EI between family members in the construction of one’s own EI. Adolescence is considered to be a crucial stage in identity construction and a time when mental health is vulnerable. Objectives: To analyze the mediating role of self-reported EI on mental health of adolescents and young adults still living in the family home, we considered the relationship between perceived EI in parents and children. Method: The sample was comprised of 170 children and their respective fathers and mothers living in the same family home. Self-reported EI was evaluated using the Trait Meta-Mood Scale (TMMS-24), whereas perceived EI was evaluated via the Perceived Emotional Intelligence Scale-24 (PTMM-24) and mental health using the MH-5. Results: Parents’ perceived EI of their children also children’s perceived EI of their parents has a direct effect on children’s mental health and an indirect effect through the EI self-reported by children. We discuss the differences in the role of mothers and fathers in emotional education and its influence on the results. Conclusions: We highlight the importance of perceived EI among family members, over and above the self-reported EI of each member, for its predictive power on the mental health of children.
Predicting corporate failure is an important management science problem. This is a typical classification question where the objective is to determine which indicators are involved in the failure/success of a corporation. Despite the importance of this problem, until now only classical machine learning tools have been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we introduce novel discerning measures to rank independent variables in a generic classification task. On the other hand, we apply boosting techniques to improve the accuracy of a classification tree. We apply this methodology to a set of European firms, considering the usual predicting variables such as financial ratios, as well as including novel variables rarely used before in corporate failure prediction, such as firm size, activity and legal structure. We show that our approach decreases the generalization error about thirty percent with respect to the error produced with a classification tree. In addition, the most important ratios deal with profitability and indebtedness, as is usual in failure prediction studies.
In the field of intelligence, parental beliefs about their children's intelligence can influence their performance (Beyer, 1999). In a particular way, this phenomenon is known as the Pygmalion effect (Furnham & Bunclark, 2006). In the area of Emotional Intelligence (EI), the research is scarce. Therefore, our objective is to study if the perceptive emotional adjustment differs according to the sex of the parents, and also to examine if this is reflected in the predictive power of the EI of the children. The sample consisted of 1005 subjects, including 335 students from the University of Castilla la Mancha and their respective fathers and mothers. According to the results of this study, we can conclude that emotional abilities of children perceived by their parents are quite close to those provided by children themselves. However, the mothers, in particular, were able to report these EI abilities more closely, showing, in comparison to fathers, a more accurate emotional adjustment with relation to their children's EI. The prediction of the EI of children varies according to the EI factor we are referring to, as well as with the sex of the parents.
Predicting corporate failure is an important management science problem. This is a typical classification question where the objective is to determine which indicators are involved in the failure or success of a corporation. Despite the complexity of the matter, a two-class problem has usually been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we apply the Adaboost.M1 algorithm to improve the accuracy of a classification tree in a multiclass corporate failure prediction problem using a set of European firms. On the other, we introduce novel discerning measures to rank independent variables in a generic classification task. Copyright International Atlantic Economic Society 2007Corporate failure prediction, Ensemble classifiers, Adaboost.M1, C10, G30, M00,
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