Emotional Intelligence Structure and Its Relationship with Life Satisfaction and Happiness: New Findings from the Bifactor Model

Abstract

Emotional intelligence (EI) has been found to generally predict subjective wellbeing (SWB) indicators such as life satisfaction and happiness. Concerning the specific abilities of trait EI, i.e., mood attention, emotional clarity and mood repair, research has largely demonstrated that emotional clarity and mood repair are the strongest predictors of SWB indicators, whereas mood attention has been relegated to a secondary role. To clarify previous inconsistencies, we tested EI by means of the bifactor model because it allows for a better comprehension of the complex nature of EI. The current paper was composed of two studies: Study 1 examined the prediction of SWB indicators by EI and its dimensions in the bifactor model; and Study 2 analysed the differences in EI and SWB indicators across university students and employees. Results of Study 1 demonstrated that the structure of EI is best represented by the bifactor model with a general e(motional)-factor and three specific emotional abilities. Mood attention was a negative predictor of SWB indicators, whereas mood repair was a positive predictor, and emotional clarity was non-significant. Study 2 showed that employees and university students did not differed in how EI predicted SWB indicators. These findings evidenced a shift in the study and measurement of EI. Further implications of this paper are discussed.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. Atienza, F. L., Pons, D., Balaguer, I., & García-Merita, M. L. (2000). Psychometric properties of the satisfaction with life scale in adolescents. Psicothema,12, 314–319.

    Google Scholar 

  2. Augusto-Landa, J. M., López-Zafra, E., De Antoñana, R. M., & Pulido, M. (2006). Perceived emotional intelligence and life satisfaction among university teachers. Psicothema,18, 152–157.

    Google Scholar 

  3. Augusto-Landa, J. M., Pulido-Martos, M., & López-Zafra, E. (2011). Does perceived emotional intelligence and optimism/pessimism predict psychological well-being? Journal of Happiness Studies,12, 463–474. https://doi.org/10.1007/s10902-010-9209-7.

    Article  Google Scholar 

  4. Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation. In P. B. Baltes & M. M. Baltes (Eds.), Successful aging: Perspectives from the behavioral sciences (pp. 1–34). New York: Cambridge University Press.

    Chapter  Google Scholar 

  5. Bar-On, R. (2006). The bar-on model of emotional-social intelligence (ESI). Psicothema,18, 13–25.

    Google Scholar 

  6. Beaujean, A. (2015). John Carroll’s views on intelligence: Bi-factor vs. higher-order models. Journal of Intelligence,3(4), 121–136. https://doi.org/10.3390/jintelligence3040121.

    Article  Google Scholar 

  7. Boden, M. T., & Thompson, R. J. (2015). Facets of emotional awareness and associations with emotion regulation and depression. Emotion,15, 399–410. https://doi.org/10.1037/emo0000057.

    Article  Google Scholar 

  8. Byrne, B. M. (1994). Structural equation modeling with EQS and EQS/windows: Basic concepts, applications, and programming. Thousand Oaks, CA: Sage.

    Google Scholar 

  9. Carroll, J. B. (1996). A three-stratum theory of intelligence: Spearman’s contribution. In I. Dennis & P. Tapsfield (Eds.), Human abilities: Their nature and measurement (pp. 1–17). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  10. Carstensen, L. L., Pasupathi, M., Mayr, U., & Nesselroade, J. R. (2000). Emotional experience in everyday life across the adult life span. Journal of Personality and Social Psychology,79, 644–655. https://doi.org/10.1037/0022-3514.79.4.644.

    Article  Google Scholar 

  11. Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling,14, 464–504. https://doi.org/10.1080/10705510701301834.

    Article  Google Scholar 

  12. Chen, F. F., Bai, L., Lee, J. M., & Jing, Y. (2016a). Culture and the structure of affect: A bifactor modeling approach. Journal of Happiness Studies,17, 1801–1824. https://doi.org/10.1007/s10902-015-9671-3.

    Article  Google Scholar 

  13. Chen, F. F., Jing, Y., Hayes, A., & Lee, J. M. (2013). Two concepts or two approaches? A bifactor analysis of psychological and subjective well-being. Journal of Happiness Studies,14, 1033–1068. https://doi.org/10.1007/s10902-012-9367-x.

    Article  Google Scholar 

  14. Chen, Y., Peng, Y., & Fang, P. (2016b). Emotional intelligence mediates the relationship between age and subjective well-being. The International Journal of Aging and Human Development,83(2), 91–107. https://doi.org/10.1177/0091415016648705.

    Article  Google Scholar 

  15. Cucina, J., & Byle, K. (2017). The bifactor model fits better than the higher-order model in more than 90% of comparisons for mental abilities test batteries. Journal of Intelligence,5(3), 27. https://doi.org/10.3390/jintelligence5030027.

    Article  Google Scholar 

  16. Cummins, R. A. (2003). Normative life satisfaction: Measurement issues and a homeostatic model. Social Indicators Research,64, 225–256. https://doi.org/10.1023/A:1024712527648.

    Article  Google Scholar 

  17. Damasio, B. F., Hauck-Filho, N., & Koller, S. H. (2016). Measuring meaning in life: An empirical comparison of two well-known measures. Journal of Happiness Studies,17, 431–445. https://doi.org/10.1007/s10902-014-9602-8.

    Article  Google Scholar 

  18. Delhom, I., Gutierrez, M., Lucas-Molina, B., & Meléndez, J. C. (2017). Emotional intelligence in older adults: psychometric properties of the TMMS-24 and relationship with psychological well-being and life satisfaction. International Psychogeriatrics,29, 1–8. https://doi.org/10.1017/S1041610217000722.

    Article  Google Scholar 

  19. Diener, E. D. (1984). Subjective well-being. Psychological Bulletin,95(3), 542–575. https://doi.org/10.1037/0033-2909.95.3.542.

    Article  Google Scholar 

  20. Diener, E. D., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment,49, 71–75. https://doi.org/10.1207/s15327752jpa4901_13.

    Article  Google Scholar 

  21. Diener, E. D., Oishi, S., & Lucas, R. E. (2003). Personality, culture, and subjective well-being: Emotional and cognitive evaluations of life. Annual Review of Psychology,54, 403–425. https://doi.org/10.1146/annurev.psych.54.101601.145056.

    Article  Google Scholar 

  22. Extremera, N., & Fernández-Berrocal, P. (2014). The subjective happiness scale: Translation and preliminary psychometric evaluation of a Spanish version. Social Indicators Research,119, 473–481. https://doi.org/10.1007/s11205-013-0497-2.

    Article  Google Scholar 

  23. Extremera, N., Ruiz-Aranda, D., Pineda-Galán, C., & Salguero, J. M. (2011a). Emotional intelligence and its relation with hedonic and eudaimonic well-being: A prospective study. Personality and Individual Differences,51(1), 11–16. https://doi.org/10.1016/j.paid.2011.02.029.

    Article  Google Scholar 

  24. Extremera, N., Salguero, J. M., & Fernández-Berrocal, P. (2011b). Trait meta-mood and subjective happiness: A 7-week prospective study. Journal of Happiness Studies,12, 509–517. https://doi.org/10.1007/s10902-010-9233-7.

    Article  Google Scholar 

  25. Fan, H. Y., Jackson, T., Yang, X. G., Tang, W. Q., & Zhang, J. F. (2010). The factor structure of the Mayer–Salovey–Caruso emotional intelligence test V 2.0 (MSCEIT): A meta-analytic structural equation modeling approach. Personality and Individual Differences,48(7), 781–785. https://doi.org/10.1016/j.paid.2010.02.004.

    Article  Google Scholar 

  26. Fernández-Berrocal, P., Extremera, N., & Ramos, N. (2004). Validity and reliability of the Spanish modified version of the trait meta-mood scale. Psychological Reports,94, 751–755. https://doi.org/10.2466/pr0.94.3.751-755.

    Article  Google Scholar 

  27. Fiori, M., Antonietti, J.-P., Mikolajczak, M., Luminet, O., Hansenne, M., & Rossier, J. (2014). What is the ability emotional intelligence test (MSCEIT) good for? An evaluation using item response theory. PLoS ONE,9(6), e98827. https://doi.org/10.1371/journal.pone.0098827.

    Article  Google Scholar 

  28. Fontaine, J. R. J. (2016). Comment: Redefining emotional intelligence based on the componential emotion approach. Emotion Review,8(4), 332–333.

    Article  Google Scholar 

  29. Frisby, C. L., & Beaujean, A. A. (2015). Testing Spearman’s hypotheses using a bi-factor model with WAIS-IV/WMS-IV standardization data. Intelligence,51, 79–97. https://doi.org/10.1016/j.intell.2015.04.007.

    Article  Google Scholar 

  30. Gohm, C. L. (2003). Mood regulation and emotional intelligence: Individual differences. Journal of Personality and Social Psychology,84, 594–607. https://doi.org/10.1037/0022-3514.84.3.594.

    Article  Google Scholar 

  31. Goldman, S. L., Kraemer, D. T., & Salovey, P. (1996). Beliefs about mood moderate the relationship of stress to illness and symptom reporting. Journal of Psychosomatic Research,41, 115–128. https://doi.org/10.1016/0022-3999(96)00119-5.

    Article  Google Scholar 

  32. Gutiérrez-Cobo, M. J., Cabello, R., & Fernández-Berrocal, P. (2017). The three models of emotional intelligence and performance in a hot and cool go/no-go task in undergraduate students. Frontiers in Behavioral Neuroscience,11, 33. https://doi.org/10.3389/fnbeh.2017.00033.

    Article  Google Scholar 

  33. Hodzic, S., Scharfen, J., Ripoll, P., Holling, H., & Zenasni, F. (2018). How efficient are emotional intelligence trainings: A meta-analysis. Emotion Review,10(2), 138–148. https://doi.org/10.1177/1754073917708613.

    Article  Google Scholar 

  34. Hughes, D. J., & Batey, M. (2017). Using personality questionnaires for selection. In H. Goldstein, E. Pulakos, J. Passmore, & C. Semedo (Eds.), The wiley blackwell handbook of the psychology of recruitment, selection and retention. Chichester: Wiley-Blackwell.

    Google Scholar 

  35. Jovanović, V. (2015). A bifactor model of subjective well-being: A re-examination of the structure of subjective well-being. Personality and Individual Differences,87, 45–49. https://doi.org/10.1016/j.paid.2015.07.026.

    Article  Google Scholar 

  36. Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods and Research,44, 486–507. https://doi.org/10.1177/0049124114543236.

    Article  Google Scholar 

  37. Keyes, C. L. M., & Grzywacz, J. G. (2005). Health as a complete state: The added value in work performance and healthcare costs. Journal of Occupational and Environmental Medicine,47, 523–532. https://doi.org/10.1097/01.jom.0000161737.21198.3a.

    Article  Google Scholar 

  38. Keyes, C. L. M., & Waterman, M. B. (2003). Dimensions of well-being and mental health in adulthood. In M. Bornstein, L. Davidson, C. L. M. Keyes, & K. Moore (Eds.), Well-being: Positive development throughout the life course (pp. 477–497). Mahwah: Erlbaum.

    Google Scholar 

  39. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press.

    Google Scholar 

  40. Koydemir, S., Şimşek, Ö. F., Schütz, A., & Tipandjan, A. (2013). Differences in how trait emotional intelligence predicts life satisfaction: The role of affect balance versus social support in India and Germany. Journal of Happiness Studies,14, 51–66. https://doi.org/10.1007/s10902-011-9315-1.

    Article  Google Scholar 

  41. Lauriola, M., & Iani, L. (2017). Peronality, positivity and happiness: A mediation analysis using a bifactor model. Journal of Happiness Studies,18, 1659–1682. https://doi.org/10.1007/s10902-016-9792-3.

    Article  Google Scholar 

  42. Lench, H. C., Darbor, K. E., & Berg, L. A. (2013). Functional perspectives on emotion, behavior, and cognition. Behavioral Sciences,3, 1–5. https://doi.org/10.3390/bs3040536.

    Article  Google Scholar 

  43. Lischetzke, T., Eid, M., & Diener, E. D. (2012). Perceiving one’s own and others’ feelings around the world: The relations of attention to and clarity of feelings with subjective well-being across nations. Journal of Cross-Cultural Psychology,43, 1249–1267. https://doi.org/10.1177/0022022111429717.

    Article  Google Scholar 

  44. Lucas, R. E., & Gohm, C. (2000). Age and sex differences in subjective well-being across cultures. In E. Diener & E. M. Suh (Eds.), Culture and subjective well-being (pp. 291–317). Cambridge: MIT Press.

    Google Scholar 

  45. Luo, D., Petrill, S. A., & Thompson, L. A. (1994). An exploration of genetic g: Hierarchical factor analysis of cognitive data from the Western Reserve Twin Project. Intelligence,18(3), 335–347. https://doi.org/10.1016/0160-2896(94)90033-7.

    Article  Google Scholar 

  46. Lyubomirsky, S. (2001). Why are some people happier than others? The role of cognitive and motivational processes in well-being. American Psychologist,56(3), 239–249. https://doi.org/10.1037/0003-066X.56.3.239.

    Article  Google Scholar 

  47. Lyubomirsky, S., & Lepper, H. S. (1999). A measure of subjective happiness: Preliminary reliability and construct validation. Social Indicators Research,46, 137–155. https://doi.org/10.1023/A:1006824100041.

    Article  Google Scholar 

  48. Maul, A. (2012). The validity of the Mayer–Salovey–Caruso emotional intelligence test (MSCEIT) as a measure of emotional intelligence. Emotion Review,4, 394–402. https://doi.org/10.1177/1754073912445811.

    Article  Google Scholar 

  49. Mayer, J. D., Caruso, D. R., & Salovey, P. (2016). The ability model of emotional intelligence: Principles and updates. Emotion Review,8(4), 290–300. https://doi.org/10.1177/1754073916639667.

    Article  Google Scholar 

  50. Mayer, J. D., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey & D. Sluyter (Eds.), Emotional development and emotional intelligence: Implications for educators (pp. 3–31). New York: Basic Books.

    Google Scholar 

  51. Mayer, J. D., & Stevens, A. A. (1994). An emerging understanding of the reflective (meta-) experience of mood. Journal of Research in Personality,28, 351–373. https://doi.org/10.1006/jrpe.1994.1025.

    Article  Google Scholar 

  52. Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika,58(4), 525–543.

    Article  Google Scholar 

  53. Mestre, J. M., MacCann, C., Guil, R., & Roberts, R. D. (2016). Models of cognitive ability and emotion can better inform contemporary emotional intelligence frameworks. Emotion Review,8(4), 322–330. https://doi.org/10.1177/1754073916650497.

    Article  Google Scholar 

  54. Miao, C., Humphrey, R. H., & Qian, S. (2017). A meta-analysis of emotional intelligence and work attitudes. Journal of Occupational and Organizational Psychology,90, 177–202. https://doi.org/10.1111/joop.12167.

    Article  Google Scholar 

  55. Mikolajczak, M., Luminet, O., Leroy, C., & Roy, E. (2007). Psychometric properties of the trait emotional intelligence questionnaire: Factor structure, reliability, construct, and incremental validity in a French-speaking population. Journal of Personality Assessment,88, 338–353. https://doi.org/10.1080/00223890701333431.

    Article  Google Scholar 

  56. Muthén, L., & Muthén, B. O. (2012). Mplus user’s guide (6th ed.). Los Angeles: Muthén & Muthén.

    Google Scholar 

  57. Nickerson, C., Diener, E., & Schwarz, N. (2011). Positive affect and college success. Journal of Happiness Studies,12, 717–746. https://doi.org/10.1007/s10902-010-9224-8.

    Article  Google Scholar 

  58. Parker, J. D., Creque, R. E., Barnhart, D. L., Harris, J. I., Majeski, S. A., Wood, L. M., et al. (2004). Academic achievement in high school: Does emotional intelligence matter? Personality and Individual Differences,37, 1321–1330. https://doi.org/10.1016/j.paid.2004.01.002.

    Article  Google Scholar 

  59. Petrides, K. V., Furnham, A., & Mavroveli, S. (2007). Trait emotional intelligence. Moving forward in the field of EI. In G. Matthews (Ed.), Emotional intelligence. Knowns and unknowns (Series in affective science). Oxford: Oxford University Press.

    Google Scholar 

  60. Reise, S. P., Moore, T. M., & Haviland, M. G. (2010). Bifactor models and rotations: Exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment,92, 544–559. https://doi.org/10.1080/00223891.2010.496477.

    Article  Google Scholar 

  61. Reise, S. P., Scheines, R., Widaman, K. F., & Haviland, M. G. (2013). Multidimensionality and structural coefficient bias in structural equation modeling: A bifactor perspective. Educational and Psychological Measurement,73(1), 5–26. https://doi.org/10.1177/0013164412449831.

    Article  Google Scholar 

  62. Roberts, R. D., Schulze, R., & MacCann, C. (2008). The measurement of emotional intelligence: A decade of progress? In G. Boyle, G. Matthews, & D. Saklofske (Eds.), The SAGE handbook of personality theory and assessment (pp. 461–482). New York: SAGE.

    Google Scholar 

  63. Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods,21, 137–150. https://doi.org/10.1037/met0000045.

    Article  Google Scholar 

  64. Rogoza, R., Truong, T. K. H., Różycka-Tran, J., Piotrowski, J., & Żemotel-Piotrowska, M. (2018). Psychometric properties of the MHC-SF: An integration of the existing measurement approaches. Journal of Clinical Psychology,74, 1742–1758. https://doi.org/10.1002/jclp.22626.

    Article  Google Scholar 

  65. Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and Personality,9(3), 185–211. https://doi.org/10.2190/DUGG-P24E-52WK-6CDG.

    Article  Google Scholar 

  66. Salovey, P., Mayer, J. D., Goldman, S. L., Turvey, C., & Palfai, T. P. (1995). Emotional attention, clarity, and repair: Exploring emotional intelligence using the trait meta-mood scale. In J. W. Pennebaker (Ed.), Emotion, disclosure, and health (pp. 125–154). Washington: American Psychological Association.

    Chapter  Google Scholar 

  67. Salovey, P., Stroud, L. R., Woolery, A., & Epel, E. S. (2002). Perceived emotional intelligence, stress reactivity, and symptom reports: Further explorations using the trait meta-mood scale. Psychology and Health,17, 611–627. https://doi.org/10.1080/08870440290025812.

    Article  Google Scholar 

  68. Sánchez-Álvarez, N., Extremera, N., & Fernández-Berrocal, P. (2015). The relation between emotional intelligence and subjective well-being: A meta-analytic investigation. Journal of Positive Psychology,11, 276–285. https://doi.org/10.1080/17439760.2015.1058968.

    Article  Google Scholar 

  69. Spearman, C. (1923). The nature of “intelligence” and principles of cognition. London: MacMillan.

    Google Scholar 

  70. Szczygieł, D., & Mikolajczak, M. (2017). Why are people high in emotional intelligence happier? They make the most of their positive emotions. Personality and Individual Differences,117, 177–181. https://doi.org/10.1016/j.paid.2017.05.051.

    Article  Google Scholar 

  71. Thoresen, C. J., Kaplan, S. A., Barsky, A. P., Warren, C. R., & de Chermont, K. (2003). The affective underpinnings of job perceptions and attitudes: A meta-analytic review and integration. Psychological Bulletin,129, 914–945. https://doi.org/10.1037/0033-2909.129.6.914.

    Article  Google Scholar 

  72. Urquijo, I., Extremera, N., & Villa, A. (2016). Emotional intelligence, life satisfaction, and psychological well-being in graduates: The mediating effect of perceived stress. Applied Research in Quality of Life,11, 1241–1252. https://doi.org/10.1007/s11482-015-9432-9.

    Article  Google Scholar 

  73. Van de Schoot, R., Lugtig, P., & Hox, J. (2012). A checklist for testing measurement invariance. European Journal of Developmental Psychology,9(4), 486–492. https://doi.org/10.1080/17405629.2012.686740.

    Article  Google Scholar 

  74. Van Praag, B. M., Frijters, P., & Ferrer-i-Carbonell, A. (2003). The anatomy of subjective well-being. Journal of Economic Behavior and Organization,51, 29–49. https://doi.org/10.1016/S0167-2681(02)00140-3.

    Article  Google Scholar 

  75. Wechsler, D. (1997). Wechsler adult intelligence scale III (3rd ed.). San Antonio: The Psychological Corporation.

    Google Scholar 

  76. Wilson, T. D., & Gilbert, D. T. (2005). Affective forecasting: Knowing what to want. Current Directions in Psychological Science,14, 131–134. https://doi.org/10.1111/j.0963-7214.2005.00355.x.

    Article  Google Scholar 

  77. Wood, J. V., Heimpel, S. A., & Michela, J. L. (2003). Savoring versus dampening: Self-esteem differences in regulating positive affect. Journal of Personality and Social Psychology,85, 566–580. https://doi.org/10.1037/0022-3514.85.3.566.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ana Blasco-Belled.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Measurement part of the structural equation models presented on Figs. 1 and 2. The estimates from Study 1 and Study 2 are separated by brackets. Following tables present standardised factor loadings obtained from: the Trait Meta-Mood Scale-24 (Table 5); Satisfaction with Life Scale (Table 6); and Subjective Happiness Scale (Table 7).

Table 5 Standardised factor loadings of the trait meta-mood scale-24
Table 6 Standardised factor loadings of the satisfaction with life scale
Table 7 Standardised factor loadings of the subjective happiness scale

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Blasco-Belled, A., Rogoza, R., Torrelles-Nadal, C. et al. Emotional Intelligence Structure and Its Relationship with Life Satisfaction and Happiness: New Findings from the Bifactor Model. J Happiness Stud 21, 2031–2049 (2020). https://doi.org/10.1007/s10902-019-00167-x

Download citation

Keywords

  • Emotional intelligence
  • Subjective wellbeing
  • Happiness
  • Life satisfaction
  • Bifactor model
  • Measurement