The importance of trait emotional intelligence and feelings in the prediction of perceived and biological stress in adolescents: hierarchical regressions and fsQCA models

Abstract

The purpose of this study is to analyze the combined effects of trait emotional intelligence (EI) and feelings on healthy adolescents' stress. Identifying the extent to which adolescent stress varies with trait emotional differences and the feelings of adolescents is of considerable interest in the development of intervention programs for fostering youth well-being. To attain this goal, self-reported questionnaires (perceived stress, trait EI, and positive/negative feelings) and biological measures of stress (hair cortisol concentrations, HCC) were collected from 170 adolescents (12-14 years old). Two different methodologies were conducted, which included hierarchical regression models and a fuzzy-set qualitative comparative analysis (fsQCA). The results support trait EI as a protective factor against stress in healthy adolescents and suggest that feelings reinforce this relation. However, the debate continues regarding the possibility of optimal levels of trait EI for effective and adaptive emotional management, particularly in the emotional attention and clarity dimensions and for female adolescents.

I’m Not Good for Anything and That’s Why I’m Stressed: Analysis of the Effect of Self-Efficacy and Emotional Intelligence on Student Stress Using SEM and QCA

“…For QCAs, raw data from the participants' responses were transformed into fuzzy set responses. First, as the literature suggested, all missing data were eliminated and all questionnaire constructs, or scores (variables), were calculated by multiplying the scores of the items (Boquera et al, 2016;Villanueva et al, 2017;Giménez-Espert et al, 2019). Then, the values were recalibrated between 0 and 1 (Ragin, 2008) by means of Claude and Christopher's fsQCA 2.5 software (2014), taking into consideration the three thresholds that the literature suggests (Woodside, 2013): 10% (low agreement or totally outside the set), 50% (intermediate level of agreement, neither inside nor outside the set), and 90% (high agreement or totally within the set).…”

“…QCA models make it possible to identify the percentage of explained variance (coverage), as well as the indicators of goodness of adjustment (consistency) (Ragin, 2008;Eng and Woodside, 2012). According to this, literature recommends the use of the two methodologies in a complementary manner, despite the differences between linear models and QCA (Seawright, 2005;Vis, 2012;Boquera et al, 2016;Villanueva et al, 2017;Castellano Rioja et al, 2019;Giménez-Espert et al, 2019). SEMs will offer different but complementary results to those provided by the QCA.…”

“…For this reason, these three dimensions should all be considered in relation with EI separately (Guerra-Bustamante et al, 2019;Martínez-Marín and Martínez, 2019). According to this, the literature suggests (Augusto-Landa et al, 2011;Serrano and Andreu, 2016) that while the EA dimension maintains a positive relationship with perceived stress (Davis and Nichols, 2016;Villanueva et al, 2017), the dimensions of EC and emotional repair (ER) show a negative relationship with the mentioned perceived stress (Aguilar et al, 2014;Ruiz-Aranda et al, 2014;Bryant and Malone, 2015;Perera and DiGiacomo, 2015;Schönfeld et al, 2016;Zeidner and Matthews, 2016). Low scores in the different components of EI are related to high levels of perceived stress (Austin et al, 2010), but in the case of EA, this relation is more complex.…”

“…Likewise, most of the existing studies have focused on the so-called linear models, which are based on the observation of linear relationships between the variables under study, obviating other types of non-linear interaction. By contrast, qualitative comparative analysis (QCA) can study beyond the individual contribution of each one of the variables to observe possible non-linear interactions between them (Villanueva et al, 2017;Castellano Rioja et al, 2019;Crespo-Hervás et al, 2019;Giménez-Espert et al, 2019).…”

To post or not to post: social media sharing and sporting event performance

“…First, all cases with missing data were removed. Second, all constructs (conditions) were calculated by multiplying their items scores (Villanueva, Montoya-Castilla, & Prado-Gascó, 2017). Third, values of the conditions were recalibrated.…”