Trait emotional intelligence Platsidou, M. (2013). Trait emotional intelligence predicts happiness, but how? An empirical study in adolescents and young adults. International Journal of Wellbeing, 3(2), 197-209. doi:10.5502/ijw.v3i2.6 Maria Platsidou University of Macedonia platsidou@uom.edu.gr Copyright belongs to the author(s) www.internationaljournalofwellbeing.org 197 ARTICLE Trait emotional intelligence predicts happiness, but how? An empirical study in adolescents and young adults Maria Platsidou Abstract: The study aimed at investigating how trait emotional intelligence (EI) dimensions intertwine to predict components of happiness. A sample of 280 high school and university students was tested with the Emotional Intelligence Scale and the Oxford Happiness Inventory. Regarding the inner structure of trait EI, findings suggested that certain dimensions (Appraisal and Utilization of emotions) predict others (Optimism/mood regulation, Social ability), thus confirming that there are lower- and higher-level dimensions in the trait EI continuum. Also, path analysis confirmed that specific EI dimensions predict happiness in a distinctive mode. First, each of the components of happiness was directly predicted by at least one EI dimension: Vigor and personal efficacy was predicted by Appraisal of emotions; Positive affect, enjoyment and fun and also Life satisfaction were predicted by both Optimism/mood regulation, and Social ability; Social interest was predicted by Social ability. Second, the happiness components were also indirectly predicted by the lower-level EI dimensions via the higher-level EI dimensions they affect. These findings suggest that focusing on developing the basic EI dimensions may be the key to designing effective training programs that will result in enhancing happiness. Keywords: emotional intelligence, happiness, Greek students, path analysis, Emotional Intelligence Scale, Oxford Happiness Inventory 1. Introduction Research in recent years has validated that emotional intelligence (EI) is significant in predicting a number of real-life outcomes, such as occupational and academic success and quality of interpersonal relations, in a differentiated manner than general intelligence or personality do (Lam & Kirby, 2002; Petrides, Frederickson, & Furnham, 2004; Petrides, Pérez- González, & Furnham, 2007). More specifically, several research findings indicate that trait EI predicts happiness and relevant dimensions, such as life satisfaction, positive affect, and depression-proneness, over and above other personality traits (e.g., those described by the Big Five personality model) (Chamorro-Premuzic, Furnham, & Lewis, 2007; Petrides & Furnham, 2003). Although this association is clear, far less is known about how the inner—possibly hierarchical—structure of trait EI dimensions affects the components of happiness. Considering the above, the present study aimed to investigate the constituent latent structure of trait EI and happiness, as tested in a sample of adolescents and young participants, and to determine how the dimensions of EI predict the components of happiness. http://creativecommons.org/licenses/by-nc-nd/3.0/ http://creativecommons.org/licenses/by-nc-nd/3.0/ http://creativecommons.org/licenses/by-nc-nd/3.0/ http://creativecommons.org/licenses/by-nc-nd/3.0/ http://creativecommons.org/licenses/by-nc-nd/3.0/ http://creativecommons.org/licenses/by-nc-nd/3.0/ Trait emotional intelligence predicts happiness, but how? Platsidou www.internationaljournalofwellbeing.org 198 1.1 Trait EI Trait EI concerns people’s self-perceptions of their emotional abilities and skills, personality characteristics and behavioral dispositions that influence their ability to cope successfully with environmental demands and pressures (Petrides, 2011). According to Petrides and his colleagues (Petrides, Pita, & Kokkinaki, 2007), the construct can alternatively be labeled as trait emotional self-efficacy; it is located at the lower levels of personality hierarchies, and it is measured via self-reports. Various theoretical models have been proposed to account for the nature, the structure and the component dimensions of trait EI and questionnaires have been designed to measure the construct(s) within each theory (e.g., Goleman, 2001; Petrides & Furnham, 2001; Petrides, Pérez-González, et al., 2007; Wong & Law, 2002). One of the most commonly used tools for measuring perceived (or trait) EI was designed by Schutte et al. (1998). Originally, the Emotional Intelligence Scale (EIS) was supposed to measure global EI; subsequent studies, having factor-analyzed the data collected with the EIS, found that it can measure specific components of EI, such as optimism and mood regulation, emotion appraisal, social skills, and utilization of emotions (Ciarrochi, Chan, & Bajgar, 2001; Petrides & Furnham, 2000; Platsidou, 2010; Saklofske, Austin, & Minski, 2003). This scale was used in the present study. 1.2 Happiness and its determinants Happiness, or “subjective wellbeing”, a term that is used synonymously, refers to the subjective assessment of quality of life or, in other words, the way people evaluate their lives. These evaluations can be both cognitive and affective, and refer to life as a whole and/or to specific domains of it, such as work and social life (Diener, Suh, Lucas, & Smith, 1999). Subjective wellbeing, or happiness, involves a number of distinct components, such as satisfaction with life as a whole or with significant life domains (e.g., satisfaction with social or work life), positive affect and low levels of negative affect (Diener, 2000). Furthermore, happiness can be operationalized in terms of self-realization or self-actualization (McGregor & Little, 1998) and it is possible to also include cognitive-evaluative elements regarding self-esteem and optimism, a sense of personal and environmental control, self-efficacy, positive relations with others, etc. (see Meleddu, Guicciardi, Scalas, & Fadda, 2012, for a review). Happiness is measured using self-report questionnaires that assess either global happiness or specific component dimensions of happiness. The Oxford Happiness Inventory (OHI; Argyle, Martin & Crossland, 1989) that was used in the present study is amongst the most popular scales. It has been used to assess either global trait happiness or its specific dimensions (e.g., life satisfaction, personal efficacy, vigor, positive affect, sociability, social interest, cheerfulness) (Argyle, Martin, & Lu, 1995; Hills & Argyle, 2002; Karademas & Kalantzi-Azizi, 2005; Meleddu et al., 2012). In determining happiness, many factors have been identified as significant (e.g., money, social relations, work, religion), with personality being the most outstanding factor (Diener & Ryan, 2009). Specifically, there is consistent evidence showing that positive and negative affect, extraversion, conscientiousness, emotional stability, agreeableness and neuroticism are strong predictors of happiness (Chamorro-Premuzic et al., 2007; DeNeve & Cooper, 1998; Hayes & Joseph, 2003; Palmer, Donaldson, & Stough, 2002). Trait EI has also been examined as a possible determinant of happiness and most studies have concluded that it predicts various components of happiness (e.g., Extramera & Fernandez-Berrocal, 2005; Kong, Zhao, & You, 2012). Given the conceptual and empirical overlap between trait EI and other personality dimensions, mostly the Big Five personality factors, many studies aimed to determine the unique contribution of Trait emotional intelligence predicts happiness, but how? Platsidou www.internationaljournalofwellbeing.org 199 each of them in predicting happiness. As most findings showed, trait EI explains variance in various measures of happiness, such as global happiness, life satisfaction, depression- proneness, and multiple types of happiness, over and above the Big Five or the Eysenck personality factors (Furnham & Christoforou, 2007; Furnham & Pertides, 2003; Petrides, Pérez- González, et al., 2007; Saklofske et al., 2003). The study of Chamorro-Premuzic et al. (2007), in particular, underscored the significant role of trait EI in predicting happiness by mediating the explanatory paths from agreeableness, stability and consciousness to happiness. In their conclusion, the authors suggest that the different dispositional determinants of happiness may be hierarchically intertwined. Whether this also applies to the inner structure of determinants of happiness, such as trait EI, deserves further exploration. In exploring the role of EI in happiness, most studies have focused on how global EI determines happiness (e.g., Furnham & Petrides, 2003; Kong et al., 2012). However, according to the study of Chamorro-Premuzic et al. (2007) reviewed earlier, the distinct EI dimensions may have a differentiated role in predicting various components of happiness. Until now, only few studies have examined the role of specific EI dimensions in predicting happiness. For example, in the studies of Extramera and Fernandez-Berrocal (2005) and Palmer et al. (2002), both of which used the Trait Meta-Mood Scale, it was found that Clarity of emotions (i.e., the perceived ability to understand and discriminate between moods and emotions) accounted for further variance in life satisfaction, which was not explained by mood states and personality traits or by positive and negative affect, respectively. 1.3 Aim and hypotheses of the present study Given all the above, the present study aimed at investigating the distinctive mode in which the different trait EI dimensions predict components of happiness. Specifically, first, regarding the relations of EI dimensions, Mayer, Salovey, and Caruso (2008, p. 506) argue that EI dimensions fall along “a continuum from those that are relatively lower level, in the sense of carrying out fundamental, discrete psychological functions, to those that are more developmentally complex and operate in the service of personal self-management and goals”. Based on this, the hypothesis tested was that EI dimensions at the lower levels of the continuum, such as the perception and appraisal of emotions, will affect the EI dimensions at the higher levels, such as utilization of emotions and emotion regulation; in turn, the latter will affect social ability, which is included among the more specialized EI skills. Even though the argument of Mayer et al. (2008) concerns the structure of ability EI models, it is plausible to assume that it may also apply to trait EI models. Second, in predicting happiness, the hypothesis tested was that specific EI dimensions, such as social ability, can predict pertinent components of happiness, such as social interest, life satisfaction and enjoyment and fun. Mood regulation and utilization of emotions can predict personal efficacy, positive affect and life satisfaction. Finally, appraisal of emotions and possibly each of the mood regulation and utilization of emotions dimensions can indirectly affects the components of happiness via their effects on higher-level EI dimensions (as suggested above). 2. Method 2.1 Participants Our sample consisted of 280 participants, aged 15 to 27 (M = 18.1, SD = 2.64), of whom 152 were high school students and 128 were university students. They were randomly recruited from Trait emotional intelligence predicts happiness, but how? Platsidou www.internationaljournalofwellbeing.org 200 their schools located at a city of Northern Greece to voluntarily participate in the study. In terms of gender, 44.6% of the participants were male and 53.9% were female (while 1.5% did not report their gender). 2.2 Research instruments Participants were asked to complete a package of self-report questionnaires which aimed to measure their trait EI and happiness. The scales used for these measurements are described below. Emotional Intelligence Scale. The Emotional Intelligence Scale (EIS; Schutte et al., 1998) consists of 33 self-referencing statements in which participants are asked to rate the extent to which they (dis)agree with each statement on a five-point Likert-type scale, ranging from 1 = strongly disagree to 5 = strongly agree. Most factor analytic studies have suggested that the EIS is not unidimensional, as Schutte et al. (1998) originally proposed, and have identified four interpretable factors in the scale (Petrides & Furnham, 2000; Platsidou, 2010; Saklofske et al., 2003). Oxford Happiness Inventory. The Oxford Happiness Inventory (OHI; Argyle, et al., 1989; Hills & Argyle, 2002) is a widely used trait happiness scale comprising 29 items. Participants are asked to respond to each of them on a five-point Likert-type scale (ranging from 1 = strongly agree to 5 = strongly disagree), with higher scores indicating greater happiness. OHI was initially designed to provide a measure of personal happiness as a whole but, in some studies, some specific dimensions of happiness have been identified: satisfaction with life, personal efficacy, sociability/empathy, a positive outlook, physical wellbeing, cheerfulness, and self-esteem (Argyle, Martin, & Lu, 1995; Hills & Argyle, 2002); mastery and self-fulfillment, satisfaction with life, vigor, social interest, and social cheerfulness (Meleddu et al., 2012); life satisfaction, vigor, and positive affect (Karademas & Kalantzi-Azizi, 2005). 3. Results 3.1 Factor structure and reliability of the inventories Both the EIS and the OHI have been used in numerous studies in the past. However, the various factor analytic solutions that were found in previous studies are not stable. Most produce multi-dimensional measures of EI and happiness, as described earlier, but there is not agreement amongst the various studies regarding the number and the nature of the component dimensions derived from each inventory. For this reason, Petrides and Furnham (2000) suggest that researchers should factor-analyze a scale before using it. Therefore, in the present study exploratory and confirmatory factor analyses were applied to determine the underlying factor structure of each inventory in this sample. Regarding the EIS, an exploratory factor analysis using varimax rotation was first applied on the data. It resulted in a four-factor model, accounting for 44.62% of the total variance: (a) Appraisal of emotions (eigenvalue = 5.41, 11.09% explained variance) refers to an accurate perc eptio n, understanding and assessment of emo tio ns i n oneself and o thers; (b) Optimism/mood regulation (eigenvalue = 2.87, 8.78% explained variance) refers to an optimistic attitude and positive coping with difficulties in one’s own or others’ life; (c) Social ability (ei genval ue = 1.82, 8.34% expl ained vari anc e) f acili tates eff ec ti ve i nterac tion and communication with others; finally, (d) Utilization of emotions (eigenvalue = 1.67, 7.42% explained variance) refers to using emotions to promote thinking. In that model, 12 items were found to load almost equally on two factors or to have low loadings on a factor (< .40), so they Trait emotional intelligence predicts happiness, but how? Platsidou www.internationaljournalofwellbeing.org 201 Table 1. The structure of the Emotional Intelligence Scale (standardized solution) in the test sample Factors Items Appraisal of emotions (F1) Optimism/ Mood regulation (F2) Social ability (F3) Utilization of emotions (F4) E R2 Aware of my emotions as experience them (EI9) .382 .924 .146 Know people's emotions by facial expressions (EI18) .790 .613 .625 Know why my emotions change (EI19) .524 .851 .275 Easily recognize my emotions (EI22) .446 .895 .199 Aware of others' non-verbal messages (EI25) .540 .842 .291 Know what others feel by looking (EI29) .723 .691 .522 Know what others feel by their voice tone (EI32) .583 .813 .339 Expect to do well on most things (EI3) .566 .824 .321 Expect good things to happen (EI10) .598 .802 .357 Motivate myself by imagining a good outcome (EI23) .635 .773 .403 Use good moods to keep trying (EI31) .582 .813 .339 Others confide in me easily (EI4) .493 .870 .243 Emotions make life worth living (EI8) .403 .915 .163 Know how to make a positive emotion last (EI12) .474 .880 .225 Arrange events others enjoy (EI13) .404 .915 .163 Compliment others when doing well (EI24) .409 .912 .167 Feel like having experienced others' events (EI26) .333 .943 .111 Help others feel better (EI30) .509 .861 .259 See new possibilities when mood changes (EI7) .552 .834 .305 New ideas when in a positive mood (EI20) .782 .624 .611 New ideas when in a change in emotions (EI27) .336 .942 .113 Factor correlations F1 – F3 F1 – F4 F2 – F3 F2 – F4 .396 .409 .468 .368 Note: The following variable residuals were allowed to covariate: E12-D2, E13-D2, E22-D2, E26-D2, E30- D2 (indicating that these items share common variance not only with the Optimism/mood regulation factor but also with the Social skills factor), E22-D3, E27-D3, D3-D4. Trait emotional intelligence predicts happiness, but how? Platsidou www.internationaljournalofwellbeing.org 202 Table 2. The structure of the Oxford Happiness Inventory (standardized solution) in the test sample Factors Items Positive affect, enjoyment & fun (F1) Vigor & personal efficacy (F2) Life satisfaction (F3) Social interest (F4) E R2 Wake up feeling rested (O2) .281 .960 .079 Feel happy (O4) .741 .671 .549 Optimistic about the future (O12) .539 .843 .290 Have a good influence on events (O15) .602 .798 .363 Have a cheerful effect on others (O17) .484 .875 .235 Find beauty in things (O18) .480 .877 .230 Experience joy and elation (O19) .769 .639 .591 Can do most things (O20) .541 .841 .292 Have fun with others (O24) .570 .821 .325 Find things amusing (O25) .590 .807 .348 Look attractive (O26) .425 .905 .181 Laugh a lot (O29) .616 .788 .379 In control of my life (O1) .520 .854 .271 Satisfied with my life (O3) .612 .791 .375 Feel energetic (O5) .601 .799 .361 Have happy memories of the past (O9) .443 .897 .196 Make decisions easily (O11) .282 .959 .080 Can organise time (O21) .362 .932 .131 Committed and involved (O22) .329 .944 .108 Life is good (O7) .807 .591 .651 Life is rewarding (O8) .617 .787 .381 World is good (O23) .549 .836 .302 Warm feelings for others (O27) .751 .661 .563 Interested in others (O28) .664 .747 .441 Factor correlations F1 – F3 F1 – F4 F2 – F3 F3 – F4 .624 .535 .689 .417 Trait emotional intelligence predicts happiness, but how? Platsidou www.internationaljournalofwellbeing.org 203 excluded from further analysis. In the next step, confirmatory factor analysis (CFA) was applied on the remaining 21 items, testing for the fit of the four-factor model. The goodness-of- fit indicators of this model were very good: Satorra-Bentler Scaled χ2 (24) = 52.25, p < .000, χ2 /df = 2.18, robust CFI = .96, SRMR = .06, and robust RMSEA = .06 (CI90% .04 to .09) (Schreiber, Nora, Stage, Barlow, & King, 2006). These findings match the four-factor solution which emerged in previous studies (e.g., Petrides & Furnham, 2000; Platsidou, 2010). The model also includes a number of interrelations between factors and few residual covariances, as shown in Table 1 (above). Cronbach’s α values indicated that two of the factors demonstrate acceptable levels of reliability (Appraisal of emotions α = .78 and Optimism/mood regulation α = .69), while the other two exhibit low reliability (Social ability α = .61 and Utilization of emotions α = .53). To investigate the latent structure of the OHI in the present sample, first, an exploratory factor analysis (with varimax rotation) was applied to the data. Four factors were identified explaining for 41.13% of the total variance: (a) Positive affect, enjoyment and fun (eigenvalue = 7.48, 15.19% explained variance), refers to experiencing a level of pleasurable engagement with the environment and positive moods such as joy, interest, and alertness; (b) Vigor and personal efficacy (eigenvalue = 1.59, 9.82% explained variance), refers to the enthusiastic and resilient strength of mind and/or body and the power to achieve a desired goal; (c) Life satisfaction (eigenvalue = 1.5, 9.28% explained variance) reflects the way a person evaluates his or her life as a whole rather than their current feelings; (d) Social interest (eigenvalue = 1.36, 6.84% explained variance) is a feeling of community and an active interest in the welfare of humankind. Five items out of the 29 loaded low (< .40) on the factors, so they were excluded from further analysis. In the next step, confirmatory factor analysis was applied on the remaining 24 items of the OHI. CFA verified the four-factor structure of the scale in this sample, with interrelations between almost all the factors, as shown in Table 2 (above). An adequate fit to the data was yielded, as the goodness-of-fit indicators show: Satorra-Bentler Scaled χ2 (246) = 379.76, p < .000, χ2 /df = 1.54, robust CFI = .90, SRMR = .06, and robust RMSEA = .04 (CI90% .04 to .05). Cronbach’s α values indicated that all factors demonstrate satisfactory or acceptable levels of reliability: Positive affect, enjoyment and fun α = .83, Vigor and personal efficacy α = .64, Life satisfaction α = .71, and Social interest α = .66. 3.2 Predicting happiness Based on the CFAs described earlier, the means of the item means for each of the EI and happiness factors were computed. The new variables—representing the four EI and the four happiness factors—were used in a path analysis aiming at examining how EI dimensions predict dimensions of happiness. The correlation matrix of EI and happiness factors is given in Table 3 (below). Path analysis was applied using the maximum likelihood robust method of estimation. According to the hypothesis, there must be paths running from the fundamental Appraisal of emotions dimension to the higher-level Utilization of emotions and Optimism/mood regulation dimensions and from these to the more specific Social ability. In the structural model that fitted the data best (shown in Figure 1 below), there were paths running from Appraisal of emotions to Utilization of emotions and from Utilization of emotions to both Optimism/mood regulation and Social ability. A path to the latter also ran from Optimism/mood regulation. Apparently, the initial hypothesis was largely confirmed. Trait emotional intelligence predicts happiness, but how? Platsidou www.internationaljournalofwellbeing.org 204 Table 3. Correlations matrix of EI and dimensions of happiness Appraisal of emotions Optimism /mood regulation Social ability Utilization of emotions Positive affect, enjoyment and fun Vigor and personal efficacy Life satisfaction EI dimensions Optimism/ mood regulation .144* Social ability .325** .340** Utilization of emotions .269** .298** .311** Happiness dimensions Positive affect, enjoyment and fun .169** .477** .537** .260** Vigor and personal efficacy .161** .459** .303** .192** .604** Life satisfaction .234** .563** .485** .334** .313** .322** Social interest .150* .212** .386** 150* .427** .268** .190** * p < .05, ** p < .01 Regarding prediction of happiness, the second hypothesis was also partially confirmed. Specifically, Vigor and personal efficacy was directly predicted by both Appraisal of emotions and Optimism/mood regulation; Positive affect, enjoyment and fun and Life satisfaction were both directly predicted by Optimism/mood regulation and Social ability; finally, Social interest was affected by Social ability. In addition to the above, indirect effects of certain EI dimensions on the components of happiness were also noted; namely, Utilization of emotions indirectly affected all components of happiness, via its effect on Optimism/mood regulation and Social ability. In turn, Optimism/mood regulation, via its effect on Social ability, exerted an indirect effect on Positive affect, enjoyment and fun and Life satisfaction (in addition to its direct effect on these variables) as well as an indirect effect on Social interest. Finally, Appraisal of emotions had a more remote, indirect effect on the components of happiness, since it predicted Utilization of emotions. As the goodness-of-fit indicators show, this model fitted excellently to the data: Satorra-Bentler Scaled χ2 (13) = 22.12, p < .000, χ2/df = 1.70, CFI = .98, SRMR = .06, and RMSEA = .05 (CI90% .00 to .08). In this model, intercorrelations were noted between (a) Appraisal of emotions and Social ability, (b) Vigor and personal efficacy and Positive affect, enjoyment and fun, (c) Positive affect, enjoyment and fun and Social interest. Trait emotional intelligence predicts happiness, but how? Platsidou www.internationaljournalofwellbeing.org 205 Figure 1. The path model of the effects of trait EI dimensions on happiness components Note: All the path coefficients are significant at the level .05 4. Discussion The present study was designed to investigate how trait EI dimensions intertwine to predict components of happiness. Regarding the inner structure of the scales used to measure EI and happiness, factor analyses confirmed that the variance of the EIS (Schutte et al., 1998) is accounted for by a four-factor model including Appraisal of emotions, Utilization of emotions, Optimism/mood regulation, and Social ability; in the OHI (Argyle et al., 1989), a four-factor model was confirmed, including Vigor and personal efficacy, Positive affect, enjoyment and fun, Life satisfaction, and Social interest. In line with our expectations, the specific direct and indirect effects of trait EI dimensions on components of happiness were significant in Greek high school and university students. This is in agreement with previous findings reporting the effect of trait EI on happiness (e.g., Chamorro-Premuzic et al., 2007; Furnham & Petrides, 2003; Gallagher & Vella-Brodrick, 2008; Kong et al., 2012). Few studies (e.g., Extramera & Fernandez-Berrocal, 2005; Palmer et al., 2002), asserted that the specific EI dimensions predict happiness in a distinctive mode. The present study extended the above to include the four trait EI dimensions and the four components of happiness described earlier; the results largely confirmed the hypothesis, as they showed that, first, each of the components of happiness was directly predicted by at least one EI dimension: Vigor and personal efficacy was predicted by Appraisal of emotions; Positive affect, enjoyment and fun as well as Life satisfaction were predicted by both Optimism/mood regulation, and Social ability; Social interest was predicted by Social ability. Trait emotional intelligence predicts happiness, but how? Platsidou www.internationaljournalofwellbeing.org 206 These findings suggest that individuals who are proficient in appraising emotions may experience a higher sense of vigor and personal efficacy; those who are optimistic and able to regulate their moods and those who are skillful in social settings tend to enjoy elevated levels of most components of happiness. Second, the components of happiness were also indirectly predicted by the EI dimensions that affected the specific EI dimensions which directly predicted them, as described earlier. In other words, findings suggest that there is an effect of the lower-level EI dimensions on the higher-level ones, thus confirming that trait EI is a “hierarchically” structured construct, as Mayer et al. (2008) asserted for ability EI. Following this line of evidence, one can assume that individuals who are able to accurately appraise their emotions can utilize their emotions skillfully; in turn, those who utilize emotions efficiently tend to be more optimistic and able to regulate their moods and to be socially skillful; finally, individuals with optimism and good mood regulation report having higher social skills. Evidently, the lower-level EI dimensions indirectly predict the happiness components via the higher-level EI dimensions they have an effect on. Consistent with the above, the studies of Extramera and Fernandez-Berrocal (2005) and Palmer et al. (2002) found that Clarity of emotions better accounted for further variance in life satisfaction than other EI dimensions such as Attention of mood and emotions, and negative Mood repair (Salovey, Mayer, Goldman, Turvey, & Palfai, 1995). Clarity of emotions as well as Appraisal of emotions are both based on the fundamental ability to accurately perceive and understand emotions, hence providing further evidence that they may be the required precondition for the adaptive utilization and regulation of emotions and moods and for efficient use of social skills (Extramera & Fernandez-Berrocal, 2005; Salovey et al., 1995). The earlier and the current findings support the notion that the inner structure of trait EI includes basic dimensions (such as Appraisal of emotions) at the lower levels of the continuum and more specific dimensions (such as Social ability) at the higher levels. Consequently, any effect within the EI dimensions is transmitted to the constructs that are affected by them, such as the components of happiness. The present study provides insight into the mechanisms that lie beneath emotional functioning and happiness. The findings suggest that focusing on developing the basic EI dimensions may be the key to designing effective training programs that will result in enhancing happiness. Should future research confirm and expand this evidence, a significant step will be taken regarding the guidance of psychological and educational interventions which aim at developing EI abilities and skills. Future research should also include a wider age range of participants, since the sample from high school and university students used in this study limits the extent to which these findings can be generalized. Also, the cross sectional nature of the data limits determining developmental changes in the present study. In future studies, data obtained from middle- and old-aged participants as well as from a longitudinal study design would enable testing for the stability of the current findings across the adult life span. Furthermore, a wider battery of measures is needed in order to validate the contribution of trait EI to happiness. For example, the same study design can be tested using different measurement scales of perceived trait EI and happiness; multiple measures of trait EI and happiness can be used in order to better define measures of the latent variables (e.g., in the present study the social interest factor is defined by only two items). Finally, since literature has concluded that personality contributes significantly in experiencing happiness (Diener & Ryan, 2009), the underlying effects of trait EI dimensions on components of happiness identified in this study Trait emotional intelligence predicts happiness, but how? Platsidou www.internationaljournalofwellbeing.org 207 should be re-examined, to take into account the role that personality holds in that grid of predictive relationships. Authors Maria Platsidou University of Macedonia platsidou@uom.edu.gr Publishing Timeline Received 8 April 2013 Accepted 28 August 2013 Published 7 October 2013 References Argyle, M., Martin, M., & Crossland, J. (1989). Happiness as a function of personality and social encounters. In J. P. Forgas, & M. Innes (Eds.), Recent advances in social psychology: an international perspective (pp. 189-203). North Holland: Elsevier. Argyle, M., Martin, M., & Lu, L. (1995). Testing for stress and happiness: The role of social and cognitive factors. In C. D. Spielberger, & I. G. Sarason (Eds.), Stress and emotion (pp. 173-187). Washington, DC: Taylor & Francis. Chamorro-Premuzic, T., Furnham, A., & Lewis, M. (2007). Personality and approaches to learning predict preference for different teaching methods. Learning and Individual Differences, 17(3), 341-350. http://dx.doi.org/10.1016/j.lindif.2006.12.001 Ciarrochi, J. V., Chan, A. Y. C., & Bajgar (2001). 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