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A meta-analysis of the relationship between emotional intelligence and academic performance in secondary education: a multi-stream comparison.

\nNicols Snchez-lvarez

  • 1 Department of Basic Psychology, Faculty of Psychology, University of Málaga, Málaga, Spain
  • 2 Department of Social Psychology, Faculty of Psychology, University of Jaén, Jaén, Spain
  • 3 Department of Social Psychology, Faculty of Psychology, University of Málaga, Málaga, Spain

This study was a quantitative meta-analysis of empirical research on the relationship between emotional intelligence (EI) and academic performance (AP) that included the three main theoretical models of EI. We conducted a computerized literature search in the main electronic databases. Forty-four of an initial 3,210 articles met the inclusion criteria. With 49 effect sizes and a cumulative sample size of 19,861 participants, we found significant heterogeneity indices indicating a variety of results. In general, the results of this study indicated a significant effect of EI on AP ( Z ¯ = 0.26). Average association between EI and AP was higher in studies measured EI as ability ( Z ¯ = 0.31), than studies measured EI as self-report ( Z ¯ = 0.24), and self-report mixed EI ( Z ¯ = 0.26). In the educational field, this meta-analysis provides information on the specific role of EI as a function of used measures. Some practical implications are discussed.


In the educational field, academic performance (AP) is the construct that has been studied most. Teaching, learning, and all the cognitive factors related to AP have been widely examined ( Pellitteri and Smith, 2007 ). Recently, one of the most analyzed research lines concerns the influence of personality factors and personal skills on achievement of AP ( Poropat, 2009 ; MacCann et al., 2019 ). In the last 20 years, a large portion of research has been guided by a recent theoretical focus on emotional abilities, specifically emotional intelligence (EI), which has been viewed as a key component of the factors that influence well-being as well as adaptive processes in specific contexts ( Zeidner et al., 2012 ). Several reviews showed the relevance of EI as a personal resource associated with health outcomes ( Martins et al., 2010 ), well-being ( Sánchez-Álvarez et al., 2016 ), and even task performance ( Miao et al., 2017 ). Likewise, literature reviews focused on analyzing the role of EI in AP have been published ( Perera and DiGiacomo, 2013 ; MacCann et al., 2019 ). These studies showed significant effects of EI in predicting AP after controlling the effects of intelligence and personality traits. In addition, EI has emerged as a strong predictor in secondary education.

Academic Performance

Academic success or performance by students in educational centers is a key goal in the development of all educational programs. AP has been commonly measured through continuous exams or evaluations, with a general consensus about the most important aspects to evaluate, such as skills, and declarative and procedural knowledge ( Ward et al., 1996 ). Although there is no common agreement for the evaluation of AP, measures of cognitive skills or declarative knowledge are the main factors evaluated ( Perera and DiGiacomo, 2013 ), and the most commonly used indicators to measure AP are usually: Grade Performance Academic (GPA), Achievement Test (AT), Grade Average (GA), Academic Achievement (AA), Standard Assessment Test (SAT), and Teacher Ratings Academic (TRA) ( Perera and DiGiacomo, 2013 ).

Recent empirical research in education regarding predictors of AP has focussed on intelligence, IQ, or personal cognitive abilities. This research movement has accumulated an extensive research literature on the measurement of cognitive intelligence ( Ritchie and Tucker-Drob, 2018 ). Moreover, there are other personal skills that differ from traditional cognitive intelligence that could affect academic success ( Furnham et al., 2009 ). Currently, there are several lines of research that analyse individual non-cognitive factors that increase the prediction of AP, which requires broader educational models that integrate personal and contextual factors ( Gutman and Schoon, 2013 ). Other non-cognitive skills include attitude, motivation, personality traits, self-regulation, resilience, and social and emotional skills, which are beyond the academic skills that determine successful performance ( Bowles and Gintis, 2007 ). Likewise, personal factors such as motivation and emotional self-regulation in the classroom are associated with school performance, that is, students who are more motivated and have greater skill to manage emotions to obtain higher academic qualifications ( Pintrich and de Groot, 1990 ). Currently, an increasing number of studies have examined the role of emotional skills such as EI in AP.

Emotional Intelligence

Since the EI concept was first introduced in the scientific literature by Salovey and Mayer (1990) , different EI models have been developed. Based on the measurement methods used, the different theoretical conceptions of EI can be grouped into three main streams: (stream 1) Mayer and Salovey (1997) four branch ability model of EI, which defines ability EI as having four components, including the capacity to perceive, value, and express emotions accurately; the ability to access and generate feelings that facilitate thinking; the ability to understand emotions and emotional awareness; and the ability to regulate emotions and promote emotional and intellectual growth; (stream 2) cognitive emotional abilities three-branch self-perception model of Salovey and Mayer (1990) , self-report EI proposes the existence of a continuous reflexive process associated with one's mood; (stream 3) cognitive emotional competences and other non-cognitive features like personal skills, motivation, and social aspects is conceived how EI mixed model ( Goleman, 1995 ; Mayer and Salovey, 1997 ; Petrides et al., 2004a ; Bar-On, 2006 ).

The ability EI stream (stream 1), also defined as EI-performance, is the conception of EI that seems to have the most similarity to AP, because EI is measured by exercises and problems to assess emotional ability, just as exams are used to measure AP in schools. On the other hand, because ability EI is assessed in a similar way to AP, students with higher levels of EI-performance could better manage stress related to exams, resulting in better AP ( Brackett and Salovey, 2006 ). At the same time, students with inadequate or poor emotional skills will have school maladjustment, interpersonal problems that affect their anxiety ( Rivers et al., 2012 ), and/or a lack of social support from their peers that affects their AP ( Mestre et al., 2006 ). The instruments developed to assess ability EI, the Mayer, Salovey, and Caruso Emotional Intelligence Test (MSCEIT) ( Mayer et al., 2002 ) and the Multifactor Emotional Intelligence Scale (MEIS) ( Mayer et al., 1999 ), have objective criteria for correct and wrong answers.

The self-report EI stream (stream 2), based on self-perception of one's emotional skills, assesses a person's subjective emotional abilities. This means that each individual indicates their level of EI according to their previous experiences and their level of self-esteem, including the mood in which they find themselves when completing the EI self-report scale ( Davies et al., 1998 ). This type of measure is usually related to well-established personality factors such as neuroticism, extraversion, agreeableness, openness, and psychoticism, and this connection can yield false correlations with performance and academic achievement ( Gannon and Ranzijn, 2005 ). Representative self-report EI instruments include the Wong and Law Emotional Intelligence Scale (WLEIS) ( Wong and Law, 2002 ), Trait Meta-Mood Scale (TMMS) ( Salovey and Mayer, 1990 ), Schutte Emotional Intelligence Scale (SEIS) ( Schutte et al., 1998 ; Saklofske and Zeidner, 2006 ), and Swinburne University Emotional Intelligence Test (SUEIT) ( Palmer and Stough, 2001 ).

In the mixed EI stream (stream 3), the integration of different personal and social skills leads to overlapping effects with other factors that may influence AP. When evaluating personality variables, cognitive skills, and social-emotional traits together, one obtains a profile that may be more associated with the different skills that are implemented in an academic context. Therefore, students with better social-emotional traits, with high cognitive abilities ( Shen and Comrey, 1997 ), and adaptive personality trait variables achieve better test scores ( Pulford and Sohal, 2006 ; Poropat, 2009 ). Therefore, students with better adaptation to the school context will obtain better scores in AP than students with profiles less oriented toward academic adaptation. Representative measures of mixed EI include the Emotional Quotient Inventory (EQi) ( Bar-On, 1997 ), Trait Emotional Intelligence Questionnaire (TEIQ) ( Petrides, 2009 ), and Emotion Identification Skills (EIS) ( Ciarrochi et al., 2008 ).

Each of the three main streams has contributed to research linking EI and AP, with heterogenous results, despite being evaluated with instruments developed under the same theoretical conceptions of EI. It is not surprising that EI is conceived from several theoretical approaches. A possible cause of the lack of consensus on the results may be the multitude of instruments to evaluate EI from the different theoretical approaches.

Theoretical Linkages Between Emotional Intelligence and Academic Performance

The EI literature has shown that individuals with a higher capacity to process information typically perform better on cognitive tasks ( Saklofske et al., 2012 ). Interpersonal and intrapersonal skills are of great importance in secondary education, since it is a period that involves many social, contextual, and personal changes and stresses. During adolescence, the peer group is of great relevance to adolescents' emotional development and identity formation ( Duncan et al., 2006 ; Eccles and Roeser, 2009 ), with immediate contexts such as the school environment being one of the most relevant ( Monreal and Guitart, 2012 ). In this sense, the events and early experiences lived in the different contexts, the reactions and responses of adolescents to the different situations of risk and stress throughout their development, as well as the existence of resource vulnerability protection, are relevant and important to understanding individual differences between young people ( Monreal and Guitart, 2012 ). Greater emotional regulation and a better process of adaptability are useful to cope with academic stress and achieve academic success ( Saklofske et al., 2012 ). Interestingly, emotional perceptive people appear to be more strongly impacted by stress than their less perceptive counterparts, expressing higher levels of psychological distress ( Ciarrochi et al., 2002 ). It is hypothesized that low perceptive people might ignore thoughts of daily hassles and therefore might be more likely to be confused about the experienced negative feelings showing less coherence between their levels of perceived stress and psychological maladjustment. Thus, people with high EI are more resilient, adapting more easily to changes, reacting better under stress conditions, and coping with difficulties in the form of challenges ( Schneider et al., 2013 ). Finally, students with a better management of their emotions are happier and have better social relationships ( Eryilmaz, 2011 ). In turn, having better interpersonal management is generally associated with higher social networks, as well as better friendships quality ( Brackett et al., 2005 ). Similarly, having a greater social network in a classroom might stimulate an adequate social environment for better cooperative work, better group learning, greater support from classmates ( Hogan et al., 2010 ), and better relationships with teachers ( Di Fabio and Kenny, 2015 ). Together, both the academic climate involving classmates and professors, as well as a better predisposition of learning-oriented abilities might be associated with a greater AP ( Brackett et al., 2011 ; Johnson, 2016 ). In summary, there are several plausible theoretical mechanisms that might explain the relationship between EI as a set of skills and optimal academic functioning in secondary education.

Current Meta-Analysis

Previous work has excluded studies conducted with instruments developed under other theoretical approaches of EI ( Perera and DiGiacomo, 2013 ), or has contemplated the role of EI in AP in a more global way and by levels ( MacCann et al., 2019 ), making it difficult to compare the results between different instruments. The present study examined the association between EI and AP, considering instruments developed from all the theoretical approaches to EI in studies conducted in secondary school students, as an educational level of greater relevance according to previous literature ( Perera and DiGiacomo, 2013 ; MacCann et al., 2019 ). Our meta-analysis aimed to examine previous review studies, comparing the results by the main streams and EI instruments used in secondary education including native English and Spanish speakers. The current meta-analysis study was carried out to (1) asses the associations of AP and EI, hypothesizing that there will be a significant correlation between EI; (2) show the associations of different instruments used to assess EI based on three main streams and levels of AP; in line with previous studies, it was hypothesized that EI ability instruments would have a greater association with AP.

Literature Search

We searched relevants studies of EI y AP on electronic database: PsychoINFO, MEDLINE, SCOPUS, PubMed, ISI Web of Science, Google Scholar, and ProQuest Dissertations and Theses. The search term (emotional intelligence) AND (academic performance OR academic achievement OR grades performance OR academic OR education OR school) AND (secondary level). We also reviewed specialized database journals of relevant papers. This review was conducted from June 2017 to January 2020.

Inclusion Criteria

Studies eligible were scanned titles and abstracts, and included in the review all those that referred to the above terms. To be included in the review, papers had to meet the following inclusion criteria for eligibility of studies ( Lipsey and Wilson, 2000 ): (1) empirical study that provides data on the association between EI and AP; (2) minimum sample size at least 20 participants; (3) studies had to have been performed between 1999 and 2020 (January); published article and unpublished doctoral thesis without published and conference paper, (4) studies written in Spanish and English.

Following a Lipsey and Wilson (2000) : (a) country, (b) publication type, (c) design features, (d) measure used to asses EI, (e) AP index, (f) study sample size, (g) size of the association between key variables, (h) level of significance. Finally, extrinsic characteristics coded were results reporting the year and publication source (see Table A1 ).

Statistical Analysis

All data were conducted in R ( Team, 2012 ), using the “stats” and “metaphor” packages ( Viechtbauer, 2010 ). For the meta-analysis the technique by DerSimonian and Laird (1986) was used. The Q -value indicated heterogeneity among studies ( p < 0.10), thus applied random effect models was used in the meta-analysis. Additionally, we quantified the effect of heterogeneity using I 2 ( Higgins and Thompson, 2002 ). The I 2 value indicate proportion of inconsistency due to heterogeneity rather than chance. The effect size index was converted by Fisher r – Z following the procedures recommended by Hedges and Olkin (1985) . The categorical model between-class results were obtained through a goodness-of-fit statistic Q b , and the within-class goodness-of-fit statistic Q w . The statistic Q wj within-category heterogeneity is under the null hypothesis of within-category homogeneity.

Publication Bias

Publication bias was evaluated by rank correlation with Kendall's tau method, in which a significant correlation indicates publication bias, and Egger's regression test asymmetry, in which significant asymmetry indicates publication bias ( Fernández-Castilla et al., 2019 ). The Egger regression test should not differ significantly ( z = −1.189, p = 0.234), and the rank correlation yielded non-significant results ( T = 0.03, p = 0.243). Non-significant results showed symmetry and absence of publication bias. Regression tests and the funnel plot indicated a non-significant asymmetry, so the results showed no evidence of publication bias between EI and AP.

Selected Studies

The sample consisted of 3,210 studies, 678 were duplicate studies. Eventually, 1,973 did not correspond to association between EI and AP. They associated lack of personal distress and absence of mental disorder to higher levels of well-being. The full text of the remaining 559 articles were reviewed, obtaining 44 items that were selected and evaluated more deeply (see Figure 1 ).

Figure 1 . PRISMA flowchart for the identification, screening, and inclusion of publications in the meta-analyses.

Study Characteristics

The articles included in the meta-analysis showed a closed association between target variables. The overall sample consisted of 19,714 participants, and the mean age was of 15.82 years. Several studies included some scales for assessing EI, obtaining 49 effect sizes. The studies included were conducted in 16 countries, with the largest number conducted in the US (14 studies).

Association of EI and AP

The main results of this study indicated that the association between EI and AP had a significant low to moderate cumulative effect ( Z ¯ = 0.26; CI from 0.14 to 0.38). A DerSimonian test and Laird's random effect showed statistical evidence of heterogeneity ( Q = 1,206.16, p < 0,001), indicating a greater variance of effect sizes between studies than anticipated by chance. In addition, the I 2 estimated of 96% suggests a high proportion of variation between samples.

Main EI Streams

The categorical model test that examined the subgroup model results intra-group showed statistical evidence of heterogeneity ( Q b = 0.39, p = 0.540). The Q w statistics revealed that the model was misspecified ( Q w = 1,205.77, p < 0.001). Therefore, significant differences were found between the effect sizes, indicating heterogeneity within each category (see Table 1 ). The ability stream showed lower levels of heterogeneity ( Q wj = 24.16, p < 0.012), with smaller variation between scores ( I 2 = 54%) obtained between the different studies that used ability stream instruments. When examining the effect size results by grouping the EI instruments by main streams, we found larger effect sizes for those studies that used instruments based on the ability EI stream ( Z ¯ = 0.31). At the same time, the degree of inconsistency between studies that used instruments based on the ability EI stream was lower ( I 2 = 54%) than in the other groups of studies (self-report EI stream I 2 = 99%; mixed EI stream I 2 = 92%).

Table 1 . Olkin and Pratt weighted average ( Z ¯ ), effect size number (K), homogeneity test ( Q wj ), and the degree of inconsistency ( I 2 ) between EI main stream.

Type of EI Measure

As shown in Table 2 , the different instruments used to assess EI had differing levels of association with AP. Moreover, there was much variability in the scores obtained in studies using the same EI instrument. Only the MSCEIT ( Q wj = 3.05, p = 0.880), SUEIT ( Q wj = 0.63, p = 0.426), and Situational Test of Emotion Management for Youths (STEM-Y) ( Q wj = 0.51, p = 0.476) measures did not show significant levels of heterogeneity between the effect sizes of the different studies. On the other hand, the largest effect sizes were observed in studies that used the Behavior Emotional Quotient Inventory (EQBI) ( Z ¯ = 0.94, K = 1), followed by the studies carried out with the MEIS ( Z ¯ = 0.50, K = 1), EIS ( Z ¯ = 0.40, K = 5), and MSCEIT ( Z ¯ = 0.35, K = 8) instruments. At the same time, the lowest degree of inconsistency between studies that used the same instruments was found for the SUEIT ( I 2 = 58%, K = 2), followed by the MSCEIT ( I 2 = 78%, K = 8), and Emotional Quotient Inventory (EQ-i) ( I 2 = 82%, K = 15), with the EQ-i being the most widely used instrument.

Table 2 . Olkin and Pratt weighted average ( Z ¯ ), effect size number (K), homogeneity test ( Q wj ), and the degree of inconsistency ( I 2 ) between EI measure.

Type of AP Measure

Subgroup analysis was conducted to examine the variability in the scores obtained in studies using the same AP instrument (see Table 3 ). The highest degree of variability in the scores between studies using the same instruments was found for the GPA ( Q wj = 246.68, p < 0.001), AA ( Q wj = 16.35, p = 0.003), and GCSE ( Q wj = 35.07, p < 0.001). Furthermore, the largest effect sizes were observed in studies using the WAEC ( Z ¯ = 0.74, K = 1), followed by the studies using the VSLECRA ( Z ¯ = 0.38, K = 1), and GPA ( Z ¯ = 0.28, K = 30) instruments. Simultaneously, the lowest degree of inconsistency between studies using the same instruments was found for the TRA ( I 2 = 47%, K = 2), followed by the AA ( I 2 = 76%, K = 5) and GPA ( I 2 = 88%, K = 30), with the GPA being the most widely used instrument.

Table 3 . Olkin and Pratt weighted average ( Z ¯ ), effect size number (K), homogeneity test ( Q wj ), and the degree of inconsistency ( I 2 ) between AP measure.

The current study was designed to examine the relationship between EI and AP through meta-analyses comparing diverse main EI streams and instruments used in secondary education. Filling the gaps in previous meta-analytic research, our study provides new data, and expands past findings. After a literature review, 44 studies with 49 independent effect sizes based on 19,714 secondary school students were included in cumulative quantitative research on the link between EI and AP. Publication bias analysis showed that these findings are robust and reliable.

Regarding hypothesis 1, we found a moderate significant cumulative effect between EI and AP, including measures of the three main EI streams, and diverse indicators of AP. These findings support previous research ( Perera and DiGiacomo, 2013 ; MacCann et al., 2019 ) suggesting that EI levels are moderately associated with academic success, which suggests that knowledge of one's own and others' feelings, as well as the ability to solve adaptive problems, provides an essential basis for academic learning ( Zeidner and Matthews, 2016 ). Additionally, these results show that EI is a personal resource with an important influence in the academic field, as a process of adaptation to the environment ( Zeidner et al., 2012 ). EI has a dual role; on the one hand, it has intrapersonal affective influences on aspects related to AP, such as motivation and self-regulation. On the other hand, interpersonal skills increase social networks in the academic environment, improving teamwork, which is so important in secondary education level. Teaching staff, through workshops can develop emotional skills to help improve mental health and interpersonal aspects, which is supported by previous literature. Current programs aim to reduce aggressive behavior and substance use; future programs should also target school performance. To deepen these interactions between emotional skills and relevant factors in AP, it would be interesting for future meta-analytical studies to focus on revealing and quantifying each of these links, especially those that are relevant at the secondary level, as it is a period full of changes, is very sensitive to risks, and involves searching for immediate well-being.

With respect to hypothesis 2, we found differences in the levels of association of EI and AP as a function of the EI measures category. The results showed non-significant differences, with ability EI measures ( Mayer and Salovey, 1997 ) showing a greater association with AP, followed by self-report EI ( Salovey and Mayer, 1990 ), and finally the mixed EI stream ( Bar-On, 2006 ). This higher index of association between EI measured with ability instruments and AP may be due to similarities with the tests used to obtain AP, as both of them use performance-based tests. In this sense it is possible this collinearity effect occurred because students who have good abilities to respond to performance tests will obtain high scores in both EI tests and tests that evaluate AP ( Ogundokun and Adeyemo, 2010 ). At the same time, and contrary to other meta-analytical studies on EI ( Martins et al., 2010 ; Sánchez-Álvarez et al., 2016 ), the most commonly used instruments in academic contexts are instruments developed from the mixed EI approach. Future studies should analyse in detail these effects of overlap and collinearity with personality and other aspects to obtain non-biased findings. Previous review studies ( Perera and DiGiacomo, 2013 ; MacCann et al., 2019 ) did not assess the impact of different measures of EI on the association with AP, so these findings provide relevant information for future studies. The results showed great heterogeneity within each instrument category, presenting large differences between different studies that used the same instrument to measure EI ( Sánchez-Álvarez et al., 2016 ). This variability could be caused by moderating variables such as sex, IQ, and personality traits, that moderate the EI–AP association when the same instruments are used ( Petrides et al., 2004b ; Furnham et al., 2005 ). Furthermore, they may be due to variations in adaptations to different languages or variations due to cultural differences ( Fernández-Berrocal et al., 2005 ; Ang and van Dyne, 2015 ). These results go beyond differences between the various instruments to evaluate EI, since they show differences despite using the same instrument. Although it is logical for each theoretical approach to develop and use its own instruments to analyse emotional skills, the results of this type of meta-analysis show the difficulties encountered when comparing the results of studies investigating this area of interest. This is certainly one of the sources of heterogeneity, and the consequent controversy about the results. To clarify this issue, it would be necessary for future studies to select instruments to evaluate emotional skills that have a robust trajectory and well-confirmed psychometric replicative properties in cross-cultural studies. Few studies have been conducted with Spanish-speaking samples. Therefore, more research is needed in Spanish and Latin American population.

The findings of this review should be considered with caution because there were several limitations. The current study was done without controlling for IQ, personality, and other variables that could influence the results. Other studies have been published in languages other than English and Spanish. On the other hand, EI integrates several dimensions, and this study did not take into account the individual associations that each of the dimensions of EI have with AP. It is possible that the associative effect of some dimensions of EI are greater than others, which implies that unifying all the dimensions of EI and analyzing the overall effect they have with AP could produce bias. Future studies should analyze each of the dimensions and their relationship with AP individually, and then compare them to analyze the differences.

These findings have several implications for research and application contexts. The school setting is one of the most important contexts for learning emotional skills and competencies ( Zeidner and Matthews, 2016 ). EI training improves other associated issues, as well as improving performance. Developing emotional skills in early stages of adolescence ( Herrera et al., 2020 ), will allow them to become consolidated personal resources to face risks and promote motivation oriented toward academic success and well-being. For this reason, this review study provides relevant information for the development of programs focused on increasing emotional skills in students, as well as providing tools for teachers and counselors, providing an empirical basis for the development of theoretical educational models oriented to AP. These findings cover the ages at which socio-emotional skills are most important, as well as relevant information for educators and teaching staff on the use of appropriate tools to assess EI in secondary education. We recommend that practitioners be cautious in choosing EI measurement instruments because of differences in their use. In the field of research, this meta-analysis provides information on which future studies should be conducted, helping to clarify the different EI concepts and evaluation measures. Future studies would need to replicate these findings with a larger sample and more of the different EI measures, including variables that may influence AP.

In conclusion, the results of this study found great heterogeneity in the outcomes assessed, so the findings should be considered with caution. The results of this meta-analysis show a moderate association between EI and AP. Future research should explore how other variables influence this relationship, improving our understanding of EI and how it influences our lives. This meta-analytic study presents a quantitative review of the association between EI and AP globally and categorically, shedding light on the gaps in previous studies on the topic on adolescents. This study also shows the inadequacies in the review of studies in this field and provides guidelines to be followed in future empirical studies on AP. These discoveries are of great relevance in the explanatory models intended to predict academic success in secondary education.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This work was supported by Instituto de Estudios Giennenses. Diputación Provincial de Jaén. Convocatoria 2018 (Ref. 2018.160.3340.45100).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Table A1 . Studies included in the meta-analysis of the relationships between EI and AP.

Keywords: emotional intelligence, academic performance, secondary education, meta-analysis, instruments

Citation: Sánchez-Álvarez N, Berrios Martos MP and Extremera N (2020) A Meta-Analysis of the Relationship Between Emotional Intelligence and Academic Performance in Secondary Education: A Multi-Stream Comparison. Front. Psychol. 11:1517. doi: 10.3389/fpsyg.2020.01517

Received: 14 April 2020; Accepted: 08 June 2020; Published: 21 July 2020.

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Copyright © 2020 Sánchez-Álvarez, Berrios Martos and Extremera. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nicolás Sánchez-Álvarez,

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William A. Haseltine Ph.D.

Understanding Your Feelings: A Study of Emotional Language

Let's unveil our common emotional language from a new quantitative study..

Posted February 20, 2024 | Reviewed by Ray Parker

  • A recent study identified four central "emotional hubs" that connect various emotion words across languages.
  • The study reveals shared emotional meanings in various languages, enhancing understanding across cultures.
  • Understanding language can improve counseling and marketing and enhance AI's understanding of human emotion.

Within the rich tapestry of human experience, emotions are the vibrant threads that weave together our most profound moments and everyday interactions. Imagine emotions as a spectrum of colors, each hue a distinct feeling that paints the canvases of our lives with joy, sorrow, passion, and fear .

This world of emotions is universal yet deeply personal, as it shapes perceptions and behaviors across cultures. Yet understanding these emotions and expressing them through language can be a challenge. Similarly, understanding others' emotions and the ways they try to express themselves can feel nearly impossible.

A recent study published in Nature Scientific Reports looked to simplify the complex world of emotions by identifying the most common emotional language. Using an extensive analysis of words that share meaning, they were able to identify four central emotional hubs to which all other emotional words have a connection:

All emotion words have a connection to one of these hubs in many languages, not just English. By searching for the common core of emotional language, the researchers uncover some of what it means to be human and express emotion.

Understanding Emotional Language

Colexification refers to the phenomenon where a single linguistic term expresses two or more distinct concepts in a language. By examining patterns of shared meaning across languages, researchers can uncover underlying cognitive and cultural associations that shape linguistic expression.

This sheds light on the universal aspects of human emotion by pinpointing common emotional hubs that underlie diverse linguistic expressions. This innovative approach enriches our understanding of language and emotion and deepens our appreciation for the rich tapestry of human communication.

Language serves as a powerful tool for shaping our perception and experience of emotions. The nuances embedded within words influence how we interpret and express our feelings. For instance, various synonyms for "love" can evoke a plethora of powerful emotional responses, each invoking a unique and profound experience.

When we speak of "adore," it conveys a deep affection and admiration, often associated with romantic infatuation. "Cherish" brings forth a sense of treasuring and holding dear, often tied to familial bonds or cherished memories. "Admire" reflects respect and esteem, acknowledging someone's qualities or achievements. "Devotion" conveys unwavering loyalty and dedication, often seen in committed relationships or religious fervor. Lastly, "passion" ignites intense desire and enthusiasm, often tied to romantic or creative pursuits. By exploring these synonyms, we uncover the intricate tapestry of emotions and connections that love encompasses.

The identification of emotional hubs illuminates the interconnectedness of emotional concepts across languages. Words associated with these hubs serve as anchors for understanding and articulating a wide array of emotional experiences. This discovery helps us understand how language and emotions are universal and can be understood beyond language barriers. This increases empathy and understanding among people.

Emotional Intelligence and Communication

Effective communication hinges on emotional intelligence —the ability to recognize, understand, and manage emotions in oneself and others. Deciphering the common emotional language underlying diverse cultures can enhance individuals' interpersonal interactions and cultivate meaningful connections.

The findings of the emotional hubs study have practical implications for fields such as psychology, counseling, and conflict resolution. Being able to navigate the shared emotional foundations across languages allows professionals to tailor their approach to resonate with diverse audiences, fostering empathy and rapport.

For example, therapists can help clients develop a more nuanced and inclusive vocabulary for expressing their feelings and experiences. This can empower clients to articulate their emotions more effectively, fostering self-awareness and emotional regulation .

There is also a business incentive to harness the power of language as it can influence engagement with companies and their products. Emotional language has the power to evoke strong emotional responses and drive customer engagement. By leveraging emotional hubs in their marketing content, businesses can create more impactful and memorable experiences that capture the attention and imagination of their audience.

research studies on emotional intelligence

Informing Language Models and Artificial Intelligence

The insights gained from identifying emotional hubs have profound implications for developing language models and artificial intelligence (AI). Language models rely on vast amounts of text data to generate human-like language and understand context. By incorporating knowledge derived from the study of shared meaning in language, language models can better understand the nuanced relationships between words and concepts, enhancing their ability to capture the complexities of human emotion and expression.

This can have many benefits, including the ability for AI to take on a more significant role in medicine , providing overworked doctors the opportunity to connect with their patients. A recent study found that AI is getting ranked with better bedside manners than some doctors due in part to language and the emotional response AI can mimic.

The recent quantitative study offers a fascinating glimpse into the intricate web of language and emotion that defines human experience. The hope is that this study will help you understand your own and others' emotions better by identifying common emotional hubs across languages.

Researchers have shed light on the universal aspects of emotional expression while acknowledging the rich diversity of human cultures. This understanding enhances our grasp of human psychology and behavior and opens doors to improved communication, empathy, and connection across linguistic and cultural boundaries .

William A. Haseltine Ph.D.

William A. Haseltine, Ph.D., is known for his pioneering work on cancer, HIV/AIDS, and genomics. He is Chair and President of the global health think tank Access Health International. His recent books include My Lifelong Fight Against Disease.

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The mediating role of academic support perception in the relationship between emotional intelligence and bullying behaviours in clinical practice: A cross-sectional study


  • 1 Nursing Department, Medical College, Hunan Normal University, Changsha, China.
  • 2 Nursing Department, Medical College, Hunan Normal University, Changsha, China. Electronic address: [email protected].
  • PMID: 38342038
  • DOI: 10.1016/j.nedt.2024.106129

Background: Bullying behaviours experienced by nursing interns in clinical practice are a considerable and serious concern. Understanding the factors that influence such behaviours in clinical practice is crucial for developing effective preventive measures and fostering a supportive learning environment.

Purpose: This study aims to investigate the prevalence and influencing factors of bullying behaviours experienced by nursing interns and examine the mediating role of academic support perception in the relationship between emotional intelligence and bullying behaviours in clinical practice.

Methods: This is a cross-sectional study that used convenience sampling. A socio-demographic information questionnaire, Bullying Behaviours in Nursing Education Scale, Wong and Law's Emotional Intelligence Scale, and Academic Support in the Practicum Scale were used to collect data from nursing interns (n = 813) at seven tertiary hospitals in Changsha, China. Binary logistic regression and mediating analyses were used to explore the factors influencing bullying behaviours in nursing practice and examine the potential mediating role of academic support perception.

Results: The prevalence of bullying behaviours in clinical practice among 813 nursing interns was 82.7 %. Binary logistic regression analyses indicated that attitude toward the nursing profession, emotional intelligence, and academic support perception were significantly associated with bullying behaviours in clinical practice. Academic support perception (β = 0.375, p < 0.001) played a significant mediating role in the relationship between emotional intelligence and bullying behaviours in clinical practice, accounting for 55.7 % of the total effect.

Conclusion: Nursing educators and administrators should recognise that improving emotional intelligence and enhancing academic support perception among nursing interns can reduce the occurrence of bullying behaviours in clinical practice.

Keywords: Academic support perception; Bullying; Clinical practice; Emotional intelligence; Nursing interns.

Copyright © 2024 Elsevier Ltd. All rights reserved.

How can we design autonomous weapon systems?

  • Original Research
  • Published: 20 February 2024

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  • Iskender Volkan Sancar   ORCID: 1  

This study examines the use of artificial intelligence in the development of autonomous weapon systems. The human involvement and responsibility aspects of the development process were examined, particularly in terms of ensuring that the system worked correctly and determining who was responsible for its actions. This paper also discusses the importance of ethical considerations such as fairness and non-discrimination when designing these systems. The study also emphasizes the importance of psychology and emotions when designing autonomous systems, as these factors can influence decisions and choices. The ability of autonomous systems to discriminate among combatants, casualties, and civilians is also an important ethical consideration. The paper concludes by emphasizing the importance of adopting a human-centered approach when designing autonomous weapon systems and recommends the use of the extended human-centered AI framework in the design of autonomous weapon systems by incorporating psychological and emotional dimensions, as it is seen as a useful tool to ensure safe, healthy, and efficient AI systems for humans. In addition, the study suggests that ACTIVE ethics, an approach to virtue ethics, can be used in the design of AWSs. When used with the extended human-centered AI framework, it is thought that more ethically appropriate and robust projects can be realized.

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Emotional Intelligence and Psychological Well-Being in Adolescents

Joan guerra-bustamante.

1 Department of Psychology, Faculty of Teacher Training College, University of Extremadura, 10071 Cáceres, Spain; se.xenu@bgnaoj (J.G.-B.); se.xenu@noelb (B.L.-d.-B.); se.xenu@zepolmv (V.M.L.-R.)

Benito León-del-Barco

Rocío yuste-tosina.

2 Department of Educational Science, Faculty of Teacher Training College, University of Extremadura, 10071 Cáceres, Spain; se.xenu@etsuyoicor

Víctor M. López-Ramos

Santiago mendo-lázaro.

The present study aimed to analyze the association between of the dimensions of emotional intelligence (attention, clarity, and repair) and different levels of perceived happiness (low, medium, and high) in adolescents. The sample consists of 646 students in the first, second, third, and fourth years of Secondary Education, 47.5% females and 52.5% males, between 12 and 17 years of age. The instruments used were the Spanish version of the Trait Meta Mood Scale-24 Questionnaire to measure perceived emotional intelligence and the Oxford Happiness Questionnaire. Multinomial logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed. The results suggest that as the capacity of understanding and regulation of emotional intelligence increases, happiness also increases. Adolescence is seen as an ideal time in life to encourage the development of emotional capacities that contribute to the greater happiness of individuals. In this way, the present study stresses the need to carry out practices leading to improvements in the adolescents’ emotional intelligence and therefore increase their happiness and emotional well-being.

1. Introduction

The study of happiness and emotional well-being in young people has expanded exponentially in recent years. Psychology has traditionally focused on unhappiness and paid little attention to positive aspects of human potential [ 1 ]. This approach has been evident when studying adolescence, since this period of life implies many changes and it has been long described as a moment of stress and difficulties [ 2 ]. This conception of adolescence is currently fairly different for studies do not only describe the adolescent as a source of problems but also as a valuable asset in a development process [ 3 , 4 ]. This change took place with the arrival of positive psychology, as one of its objectives is to promote psychological research and practice in such areas as positive traits (strengths), positive emotions, and their contribution to well-being [ 5 ].

1.1. Happiness or Psychological Well-Being

As for the study of happiness, it is essential to point out that there is no consensus about how to define it. One of the most accepted theoretical approaches states that the construct happiness refers to an emotional and cognitive type of psychological state [ 6 ], a positive affective component in which positive emotions and the subjective interpretation of well-being are fundamental [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ].

On a theoretical level, the debate on happiness has two main approaches: 1) the hedonic approach, that affirms that happiness is the presence of positive affection and the absence of negative affection; and 2) the eudaimonic approach, that states that happiness is the consequence of full psychological functioning by means of which the person develops his or her potential [ 13 ]. In line with eudaimonism, it is noteworthy to mention the psychological well-being multidimensional model [ 14 ], focused in the fulfillment of human potential through six key features: autonomy, environmental control, personal growth, positive relationships with others, purpose in life, and self-acceptance [ 15 ]. Both approaches can be integrated in the “three dimensions of happiness” model [ 1 ] which are: 1) a pleasant life, understood as a pleasant feeling towards past, present and future; 2) a committed life, by using positive individual features, including character strengths and talents; and 3) a meaningful life, which means to serve and to belong to positive institutions. Subsequently, this model favored the appearance of 24 Strengths Model [ 16 ] which focuses on studying happiness in strengths and virtues.

Accordingly, they reinforce the idea of the existence of factors that determine happiness [ 17 ]. Then we find the Science of Happiness [ 12 ] which claims that happiness can be increased by the individual himself by means of certain activities. For that matter, such a vital period as adolescence is the ideal moment to increase it. In recent years, different theoretical approaches have defended a positive comprehension of adolescence, a crucial stage characterized by plasticity, the acquisition of competences and the achievement of satisfactory levels of well-being and positive adjustments [ 17 ]. It is a time when the capacity to appreciate satisfaction with life and well-being increases in a critical and conscious way [ 18 ]. Specifically, teaching adolescents to be happy functions with three main goals: as an antidote against depression, as a means of increasing life satisfaction, and as a way to enhance learning and creative thought [ 19 ].

1.2. Emotional Intelligence

One of the variables that could help to this increase of happiness during adolescence can be emotional intelligence [ 20 ]. There are two relevant models of emotional intelligence: Mixed Models and Ability Model. Mixed Models state that emotional intelligence is a compendium of stable personality features, socio-emotional competences, motivational aspects, and different cognitive abilities [ 21 , 22 , 23 ]. On the other side we find the Ability Model [ 24 ] which considers emotional intelligence as an ability focused on emotional information processing [ 25 ]. Ever since Model of Emotional Intelligence, this construct is defined as a type of social intelligence that involves the ability to monitor one’s own and others’ emotions, to discriminate among them, and to use the information to guide one’s thinking and actions [ 24 ]. Subsequently, said authors included in their definition abilities related to cognitive and emotional clarity, perception, and repair that could generate feelings that eased thinking and abilities of cognitive and emotional regulation [ 26 ]. In order to measure this construct, they designed questionnaire TMMS-24, which assesses Perceived Emotional Intelligence through three factors: attention to emotions (capability to feel and express feelings properly), emotional clarity (capability to understand the own emotional states), and emotional repair (capability to correctly regulate emotional states).

1.3. Happiness or Psychological Well-Being and Emotional Intelligence

Scientific literature highlights the major role of emotional intelligence when determining individual happiness [ 20 ]. Numerous researchers have related emotional intelligence with psychological constructs that are closely associated with happiness, such as subjective well-being [ 27 , 28 ], higher rates of positive emotional states and decrease of negative emotional states [ 29 ], satisfaction with life [ 20 , 30 , 31 , 32 ], better psychological functioning and social competence [ 33 ], and better social relations; and negative associations with loneliness [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Other studies have focused on the relationship between emotional intelligence and variables connected with well-being in young people, such as physical and mental health [ 41 , 42 , 43 ] and perception of stress [ 44 ]. There is therefore clear evidence that capacities of emotional intelligence predict aspects related to personal well-being and a positive relation between life satisfaction and subjective happiness [ 45 , 46 ].

For this matter, Hills and Argyle [ 47 ] composed the Oxford Happiness Questionnaire, which evaluates subjective happiness from these psychological dimensions, including items focused on life satisfaction, positive emotions, physical and mental health, or social relationships.

More specifically, studies made from mixed models note that the trait emotional intelligence is a constellation of capacities and self-perceived attitudes related with emotion [ 48 ]. In this regard, different studies note the existence of a positive correlation between emotional intelligence as a trait and perceived happiness [ 49 , 50 ]. On the other hand, from the ability model, research based on Spanish adolescent subjects shows that the abilities of clarity and repair are positively correlated with life satisfaction whereas attention correlates negatively in adolescents [ 51 ]. In the same way, the dimensions of emotional recognition and expression, and the control of emotions mediate in the relationship between fully dispositional mindfulness and subjective happiness [ 52 ]. However, it should be considered that self-perceptions and attitudes associated with people’s emotions—such as emotional regulation, relationship skills, and social competence—determine variation in happiness to a large degree [ 50 ]. Henceforth, research shows that emotional intelligence abilities imply a skill that allows adolescents to guide their thoughts and ponder over their emotions, helping them to improve their well-being levels [ 53 ]. These studies suggest that important interventions may be performed to promote flourishing and happiness, enhancing emotional intelligence through specific training [ 54 ].

The present study seeks to analyze in a sample of adolescents, the association between of the dimensions of emotional intelligence (attention, clarity, and repair) and different levels of perceived happiness (low, medium, and high). It will also identify the sensitivity and the ability to distinguish scores obtained in the Spanish version of the questionnaire Trait Meta Mood Scale [ 55 ], from which high happiness is more likely to exist.

2. Materials and Methods

2.1. participants.

The sample consists of 646 students in the first, second, third, and fourth years of Secondary Education, 47.5% females and 52.5% males, between 12 and 17 years of age. The sampling was carried out by selecting eight schools in the Community of Extremadura (Spain) at random.

2.2. Instruments

2.2.1. trait meta mood scale.

The Spanish version of the questionnaire Trait Meta Mood Scale (TMMS-24) [ 55 ] has been used to evaluate perceived emotional intelligence. The questionnaire is formed by 24 items with a Likert-type five-point answer scale (1 = Do not agree, 5 = Totally agree). Three dimensions are evaluated (eight items per dimension): attention (ability to feel and express feelings appropriately); clarity (understanding of emotional states); and repair (appropriate emotional regulation). Each dimension can be classified into three traits depending on the score: Attention; 1) Attention should be improved; 2) Adequate attention; 3) Excessive attention: Clarity; 1) Clarity should be improved; 2) Adequate clarity; 3) Excellent clarity: Repair; 1) Repair should be improved; 2) Adequate repair; 3) Excellent repair. The internal consistency measured with Cronbach’s alpha was 0.826 for attention, 0.825 for clarity, and 0.833 for repair.

2.2.2. Oxford Happiness Questionnaire

The Oxford Happiness Questionnaire (OHQ) [ 47 ]. The objective of this questionnaire is to measure happiness in general, i.e., psychological well-being. A series of statements about happiness are given and the participants indicate their degree of agreement with each one. In psychometric terms, it consists of 29 items or 29 potential sources of happiness and the participants consider the extent to which they form part of their experiences. It employs a six-point Likert-type scale (1 = I totally disagree, 6 = I totally agree). The lowest score that can be obtained is 1 (if Answer 1, ‘I totally disagree’ is chosen in all the statements) and the highest is 6 (if Answer 6; ‘I totally agree’ is chosen for all the statements). In this study, the internal consistency measured with Cronbach’s alpha was 0.800.

2.3. Procedure

The procedure followed for data collection was the administration of the questionnaires by classroom group. In the first place, the educational centers were contacted to explain the objectives of the study and request authorization for the completion of the questionnaires. We followed the ethical guidelines of the American Psychological Association regarding the informed consent of the parents, due to participants’ being underage. Likewise, anonymity in the answers, the confidentiality of the obtained data, and its exclusive use for research purposes was assured. The administration of the questionnaires was carried out during school hours; it took around 50 min. in an adequate climate and without distractions. This study was approved by the Bioethics and Biosafety Committee of the University of Extremadura (no. 0063/2018).

2.4. Statistical Analysis

Firstly, we submitted the data to the assumptions of independence, normality, homoscedasticity and linearity required by the classical linear model. We did not find normality or homoscedasticity in our data, so we decided to perform a multinomial logistic regression analysis. Although it may seem that transforming a variable initially classified as continuous to categorical would mean losing information, during the analysis we gain efficiency and, mostly, clarity for interpretation. Multinomial logistic regression analysis was performed to determine the degree of association between the variables being studied. The odds ratio and their 95% confidence intervals, and the receiver operating characteristic (ROC) curve were calculated. The analysis based on the ROC curves is a statistical method to determine the diagnostic preciseness of tests that use continuous scales, and are used for three specific purposes: to establish the cut-off point at which the highest sensitivity and specificity is reached; evaluate the discriminative capacity of the diagnostic test, i.e., its capacity to differentiate healthy and sick individuals; and to compare the discriminative capacity of two or more diagnostic tests that express their results as continuous scales.

In order to verify that emotional intelligence is associated with happiness, multinomial logistic regression analysis included happiness as a predictor variable, grouped according to a criterion of percentiles in low, medium, and high happiness and the emotional intelligence dimensions attention, clarity, and repair as predictor variables, grouped in three categories ( Table 1 ). Gender and age of participants were included as control variables.

Categorization and frequencies of the study variables and descriptive statistics of the OHQ-SF questionnaire.

M = mean, SD = standard deviation. P = Percentile.

Both multinomial regression analyses demonstrated a satisfactory fit, χ 2 (16, N = 629) = 104.922, p < 0.001 (two-tailed), ϕ = 0.048; R Nagelkerke = 0.181, enabling correct classification in 62% of the cases.

The detailed analysis of the findings according to the different emotional intelligence dimensions shows the association between happiness and perceived intra-personal emotional intelligence, so that as clarity and repair increase, the individuals see themselves as happier, and as they decrease the individuals are less happy.

To be precise, for the result of the model with the reference category happiness ( Table 2 ), the calculations of the parameters reveal that adequate clarity (Wald = 4.205, p = 0.040), adequate repair (Wald = 8.609, p = 0.003), adequate repair (Wald = 14.759, p < 0.001), and excellent repair (Wald = 8.503, p =0.004) are associated significantly and directly with medium happiness. In addition, adequate (Wald = 10.376, p = 0.001) and excellent clarity (Wald = 8.610, p = 0.003), and adequate (Wald = 15.997, p < 0.001) and excellent repair (Wald = 25.323, p < 0.001) are correlated directly and significantly with high happiness.

Multinomial logistic regression model examining the probability of perceiving low happiness according to the degree of emotional attention, clarity, and repair.

Reference categories: 1 Low happiness. Groups compared: 2 little attention: 3 should improve clarity; 4 should improve repair. * p < 0.05. OR: odds ratio. CI: confidence interval.

The OR calculations of the model with the reference category low happiness ( Table 2 ) show that the probability of medium happiness is twice as high among individuals with adequate clarity, 3.4 times higher with excellent repair and 2.5 times higher with adequate repair. Similarly, the probability of high happiness is 2.7 times higher with adequate clarity, 4.1 times higher with adequate repair, 5.6 times higher with excellent clarity, and 12 times higher with excellent repair.

In addition, calculations of the parameters for the reference category high happiness ( Table 2 ) reveal that the need to improve clarity (Wald = 8.610, p = 0.003), repair (Wald = 25.323, p < 0.001), and adequate repair (Wald = 6.281, p = 0.012) are associated significantly and directly with low happiness. Equally, the need to improve clarity (Wald = 9.771, p = 0.002) and repair (Wald = 11.861, p = 0.001), and adequate clarity (Wald = 7.082, p = 0.008) and repair (Wald = 8.358, p = 0.004) are correlated directly and significantly with medium happiness.

The OR calculations of the model with the reference category high happiness ( Table 3 ) show that the probability of low happiness is 5.6 times higher among individuals who should improve clarity, 12 times higher among those who should improve repair and 3 times higher with adequate repair. Similarly, the probability of medium happiness is 3.5 times higher among individuals who should improve clarity and repair, 2.6 times higher with adequate clarity, and 2.2 times higher with adequate repair.

Multinomial logistic regression model examining the probability of perceiving high happiness according to the degree of emotional attention, clarity, and repair.

Reference categories: 1 High happiness. Groups compared: 2 Excessive attention; 3 Excellent clarity; 4 Excellent repair. * p < 0.05. OR odds ratio. CI confidence interval.

In addition, a receiver operating characteristic (ROC) curve was analyzed to assess the discriminative accuracy of the emotional intelligence dimensions. This allowed the identification of the cut-out points of the emotional intelligence scores beyond which high happiness becomes more likely.

In the ROC analysis, in the non-parametric case, the curve of the clarity dimension has an area below it of 0.696, 95% CI (0.644, 0.748), p < 0.001, and the repair dimension has below it an area of 0.707, 95% CI (0.656, 0.758), p < 0.001, while in the case of the attention dimension, the area below the curve of 0.536, 95% CI (0.478, 0.595), p = 0.206, does not provide significant information ( Figure 1 ).

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ROC curve for the TMMS-24 dimensions predicting the presence of high happiness.

The cut-off points that simultaneously optimize sensitivity and specificity, and the separate cut-off points that optimize sensitivity and specificity of the clarity and repair dimensions are shown in Table 4 .

Sensitivity, specificity and Youden Index for the scores of the clarity and repair dimensions in the TMMS-24.

*** Score that maximizes sensitivity and specificity at the same time. * Score that maximizes sensitivity. ** Score that maximizes specificity.

To identify high happiness, a score of 28.5 or over in the clarity dimension simultaneously maximizes sensitivity (60%) and specificity (71%) (Youden Index = 0.314). A score of 25.5 maximizes sensitivity (72%) while specificity remains higher than expected by random, and a cut-off point of 29.5 maximizes specificity (77%) while sensitivity remains higher than expected by random ( Table 4 ). Similarly, a point of 27.5 or over in the repair dimension simultaneously maximizes sensitivity (78%) and specificity (55%) (Youden Index = 0.333). A score of 26.5 maximizes sensitivity (79%) while specificity remains higher than expected by random, and a cut-off point of 32 maximizes specificity (76%) while sensitivity remains higher than expected by random ( Table 4 ).

4. Discussion

The present study has aimed to analyze the relationship between the dimensions of emotional intelligence (attention, clarity, and repair) and happiness in a sample of adolescents and identify the cut-off points in the emotional intelligence scores, above which high happiness is more likely.

The detailed analysis of the results demonstrates a clear association between emotional intelligence and happiness. In general, these results agree with other research analyzing the association between emotional intelligence and happiness [ 46 , 56 ] or variables connected with it, such as personal and social adjustment [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. To be precise, our results show that as emotional clarity and repair increase the individuals perceive themselves to be happier, and when they decrease they are less happy. No association has been found with the attention dimension. They agree with studies on adolescent populations that have found correlations between emotional clarity and repair, but not emotional attention, and variables closely related to happiness, such as well-being and psychological health [ 57 , 58 , 59 ] and quality of life [ 60 ].

This positive relation between happiness and emotional clarity and repair factors show that both abilities are indicators of a better emotional adjustment in adolescents [ 61 , 62 , 63 ]. Thus, the scores for clarity and repair above which happiness is maximized are situated within the established ranges for adequate emotional clarity and repair [ 55 ]. The results underscore that emotional repair has a greater association with happiness. In this line, several researchers have noted that the repair of emotions is fundamental for appropriate psychological functioning and mental health [ 64 , 65 , 66 , 67 ]. Adolescents with higher levels of emotional repair tend to carry out pleasant distracting activities, which can contribute to a greater feeling of happiness [ 68 ].

However, the question is: why is emotional attention not related to happiness? Although emotional attention is necessary for adaptation, paying too much attention to emotions is usually associated with maladaptive factors incompatible with happiness, such as anxiety, depression, hypervigilance, rumination, and catastrophization [ 32 , 33 , 51 ]. Therefore, from this point of view, excessive attention must be associated with low happiness. In contrast, emotional attention implies being aware of the feelings that produce pleasure (happiness) or discomfort (unhappiness). All emotions have a positive function and situations that cause discomfort are inevitable. Therefore, happiness cannot depend on their absence, but on a balance between the quantity and intensity of pleasant/unpleasant. In such a way, people who pay too much attention to their emotions and moods and do not have an adequate emotional clarity and repair would not be capable enough to understand and regulate the different emotional states [ 69 , 70 , 71 , 72 ].

Study Limitations

This was a transversal study; therefore, causal associations cannot be made. Likewise, the sample used and its size restricts generalizability of results. In addition, on the one hand, using the perceptions that the individuals have of their own capacities and feelings hinders the possibility of controlling possible respondent bias. It would therefore be useful to combine their own replies to the questionnaire with tests that are able to evaluate real aptitudes to solve emotional problems. On the other, although the criterion of assigning percentiles to the groups of high, medium, and low happiness allows comparisons to be made between happier or less happy individuals, it does not guarantee the identification of the happy and unhappy individuals, and consequently the results should be interpreted with a degree of caution. Despite these limitations, this study makes interesting contributions to understanding the association between emotional intelligence and happiness.

5. Conclusions

The conclusions of the present study support the idea that some capacities may help to increase the attainment of health and emotional well-being during adolescence. More precisely, it has shown that as adolescents’ capacities of comprehension and emotional regulation increase, so does their subjective happiness. The important role of emotional regulation should be stressed because it is an additional factor associated with happiness.

Finally, we are aware that the educational context is the best setting in which to establish policies promoting emotional health and well-being that can reach all the students and put an end to possible inequalities in the learning of those resources. This study has attempted to determine the specific dimensions that should be focused on when teaching emotional capacities as a variable promoting happiness and emotional well-being and health during this key period of life. To be exact, the capacities of understanding and regulating emotions can be developed and increased in adolescents as a way for their perception of their own happiness to increase.

Author Contributions

J.G.-B., B.L.-d.B., and S.M.-L. designed the study and they had full access to all the data in the study. B.L.-d.B. and S.M.-L. performed all statistical analyses and the interpretation of the data. J.G.-B., B.L.-d.B., R.Y.-T., V.M.L.-R., and S.M.-L. took part in the conduct of the survey and contributed to manuscript preparation. All authors have read and approved the final manuscript.

This work has been funded by the support to Consolidation of Research Groups (Junta de Extremadura GR18091/18.HJ.11). The authors would like to thank their support.

Conflicts of Interest

The authors declare no conflict of interest.


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