Perceived emotional intelligence, general intelligence and early professional success: predictive and incremental validity

Although the study of factors affecting career success has shown connections between biographical and other aspects related to ability, knowledge and personality, few studies have examined the relationship be-tween emotional intelligence and professional success at the initial career stage. When these studies were carried out, the results showed significant relationships between the dimensions of emotional intelligence (emotional self-awareness, self-regulation, social awareness or social skills) and the level of professional competence. In this paper, we analyze the relationship between perceived emotional intelligence, measured by the Trait Meta-Mood Scale (TMMS-24) questionnaire, general intelligence assessed by the Cattell factor "g" test, scale 3, and extrinsic indicators of career success, in a sample of 130 graduates at the beginning of their careers. Results from hierarchical regression analysis indicate that emotional intelligence makes a specific contribution to the prediction of salary, after controlling the general intelligence effect. The perceived emotional intelligence dimensions of TMMS repair, TMMS attention and sex show a higher correlation and make a greater contribution to professional success than general intelligence. The implications of these results for the development of socio-emotional skills among University graduates are discussed.

General mental ability as moderator of personality traits as predictors of early career success

In this paper, we examine the effects of general mental ability (GMA) and the personality traits defined in the big five model on extrinsic and intrinsic indicators of career success, in a sample of 130 graduates who were in the early stages of their careers. Results from hierarchical regression analyses indicated that GMA does not predict any of the success indicators. In contrast, the combination of GMA and three of the Big Five Personality traits, conscientiousness, neuroticism, and openness, is significantly associated with greater early career success and has incremental predictive validity.

Tailoring the surface chemistry of zeolite templated carbon by electrochemical methods

One option to optimize carbon materials for supercapacitor applications is the generation of surface functional groups that contribute to the pseudocapacitance without losing the designed physical properties. This requires suitable functionalization techniques able to selectively introduce a given amount of electroactive oxygen groups. In this work, the influence of the chemical and electrochemical oxidation methods, on the chemical and physical properties of a zeolite templated carbon (ZTC), as a model carbon material, have been studied and compared. Although both oxidation methods generally produce a loss of the original ZTC physical properties with increasing amount of oxidation, the electrochemical method shows much better controllability and, unlike chemical treatments, enables the generation of a large number of oxygen groups (O = 11000- 3300 μmol/g), with a higher proportion of active functionalities, while retaining a high surface area (ranging between 1900-3500 m2/g), a high microporosity and an ordered 3-D structure.

A "Know-How vs. Know-What" Approach in the Teaching-Learning of Competences in Physical Chemistry

The methodological approach a teacher uses in the competence teaching-learning process determines the way students learn. Knowledge can be acquired from a series of perspectives, mainly: “know-what” (concept), where facts and descriptions of (natural or social) phenomena are pursued; “know-how” (procedure), where methods and procedures for their application are described; and “know-why” (competence), where general principles and laws that explain both the facts and their applications are sought. As all the three cases are interconnected, the boundaries between them are not fully clear and their application uses shared elements. In any case, the depth of student’s acquired competences will be directly affected by the teaching-learning perspective, traditionally aiming to a “know-why” approach for full competence acquisition. In this work, we discuss a suitable teaching-learning methodology for evaluating whether a “know-how”, “know-what” or combined approach seems better for enhancing competence learning in students. We exemplify the method using a selection of formative activities from the Physical Chemistry area in the Grades of Chemistry and Chemical Engineering.

Does Trait Emotional Intelligence Predict Unique Variance in Early Career Success Beyond IQ and Personality?

In order to determine the contribution of emotional intelligence (EI) to career success, in this study, we analyzed the relationship between trait EI (TEI), general mental ability (GMA), the big five personality traits, and career success indicators, in a sample of 130 graduates who were in the early stages of their careers. Results from hierarchical regression analyses indicated that TEI, and especially its dimension “repair,” has incremental validity in predicting one of the career success indicators (salary) after controlling for GMA and personality. These findings provide support for the use of TEI measures as predictors of career success in the early stage.

Individual differences in predicting occupational success: The effect of population heterogeneity

Using a sample of 339 university graduates from the University of Alicante (Spain) three years after completion of their studies, we studied the relationships between general intelligence (GI), personality traits, emotional intelligence (EI), academic performance, and occupational attainment and compared the results of conventional regression analysis with the results obtained from applying regression mixture models. The results reveal the influence of unobserved population heterogeneity (latent class) on the relationship between predictors and criteria and the improvement in the prediction obtained from applying regression mixture models compared to applying a conventional regression model.