Evaluating the higher-order structure of the Profile of Emotional Competence (PEC): Confirmatory factor analysis and Bayesian structural equation modeling


Autoři: Yuki Nozaki aff001;  Alicia Puente-Martínez aff002;  Moïra Mikolajczak aff003
Působiště autorů: Department of Human Science, Faculty of Letters, Konan University, Kobe, Japan aff001;  Department of Social Psychology and Methodology of Behavior Sciences, University of the Basque Country, Lejona, Spain aff002;  Department of Psychology, Université catholique de Louvain, Louvain-la-Neuve, Belgium aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225070

Souhrn

Emotional competence (EC) reflects individual differences in the identification, comprehension, expression, regulation, and utilization of one’s own and others’ emotions. EC can be operationalized using the Profile of Emotional Competence (PEC). This scale measures each of the five core emotional competences (identification, comprehension, expression, regulation, and utilization), separately for one’s own and others’ emotions. However, the higher-order structure of the PEC has not yet been systematically examined. This study aimed to fill this gap using four different samples (French-speaking Belgian, Dutch-speaking Belgian, Spanish, and Japanese). Confirmatory factor analyses and Bayesian structural equation modeling revealed that a structure with two second-order factors (intrapersonal and interpersonal EC) and with residual correlations among the types of competence (identification, comprehension, expression, regulation, and utilization) fitted the data better than alternative models. The findings emphasize the importance of distinguishing between intrapersonal and interpersonal domains in EC, offer a better framework for differentiating among individuals with different EC profiles, and provide exciting perspectives for future research.

Klíčová slova:

Behavior – Emotions – Factor analysis – Maximum likelihood estimation – Mental health and psychiatry – Personality – Personality traits – Research validity


Zdroje

1. Saarni C. Emotional competence: How emotions and relationships become integrated. In: Thompson RA, editor. Nebraska Symposium on Motivation, 1988: Socioemotional development. Lincoln, NE: University of Nebraska Press; 1990. p. 115–82.

2. Hodzic S, Scharfen J, Ripoll P, Holling H, Zenasni F. How efficient are emotional intelligence trainings: A meta-analysis. Emot Rev. 2018; 10: 138–48. doi: 10.1177/1754073917708613

3. Kotsou I, Nelis D, Gregoire J, Mikolajczak M. Emotional plasticity: Conditions and effects of improving emotional competence in adulthood. J Appl Psychol. 2011; 96: 827–39. doi: 10.1037/a0023047 21443316

4. Nelis D, Kotsou I, Quoidbach J, Hansenne M, Weytens F, Dupuis P, et al. Increasing emotional competence improves psychological and physical well-being, social relationships, and employability. Emotion. 2011; 11: 354–66. doi: 10.1037/a0021554 21500904

5. Szczygiel D, Mikolajczak M. Is it enough to be an extrovert to be liked? Emotional competence moderates the relationship between extraversion and peer-rated likeability. Front Psychol. 2018; 9: 804. doi: 10.3389/fpsyg.2018.00804 29875728

6. Nozaki Y, Koyasu M. Can we apply an emotional competence measure to an eastern population? Psychometric properties of the Profile of Emotional Competence in a Japanese population. Assessment. 2016; 23: 112–23. doi: 10.1177/1073191115571124 25670840

7. Nozaki Y. Emotional competence and extrinsic emotion regulation directed toward an ostracized person. Emotion. 2015; 15: 763–74. doi: 10.1037/emo0000081 25938611

8. Keefer KV. Self-report assessments of emotional competencies: A critical look at methods and meanings. J Psychoeduc Assess. 2015; 33: 3–23. doi: 10.1177/0734282914550381

9. Mayer JD, Salovey P. What is emotional intelligence? In: Salovey P, Sluyter D, editors. Emotional development and emotional intelligence: Educational implications. New York, NY: Basic Books; 1997. p. 3–31.

10. Petrides KV, Furnham A. Trait emotional intelligence: Psychometric investigation with reference to established trait taxonomies. Eur J Personality. 2001; 15: 425–48. doi: 10.1002/per.416

11. Bar-On R. The emotional intelligence inventory (EQ-i): Technical manual. Toronto, ON, Canada: Multi-Health Systems.; 1997.

12. Mikolajczak M, Petrides KV, Coumans N, Luminet O. The moderating effect of trait emotional intelligence on mood deterioration following laboratory-induced stress. Int J Clin Health Psychol. 2009; 9: 455–77.

13. Lumley MA, Gustavson BJ, Partridge RT, Labouvie-Vief G. Assessing alexithymia and related emotional ability constructs using multiple methods: interrelationships among measures. Emotion. 2005; 5: 329–42. Epub 2005/09/29. doi: 10.1037/1528-3542.5.3.329 16187868

14. Petrides KV, Pita R, Kokkinaki F. The location of trait emotional intelligence in personality factor space. Br J Psychol. 2007; 98: 273–89. doi: 10.1348/000712606X120618 17456273

15. Harms PD, Credé M. Emotional intelligence and transformational and transactional leadership: A meta-analysis. J Leadersh Organ Stud. 2010; 17: 5–17. doi: 10.1177/1548051809350894

16. Martins A, Ramalho N, Morin E. A comprehensive meta-analysis of the relationship between emotional intelligence and health. Pers Indiv Differ. 2010; 49: 554–64. doi: 10.1016/j.paid.2010.05.029

17. Miao C, Humphrey RH, Qian S. Are the emotionally intelligent good citizens or counterproductive? A meta-analysis of emotional intelligence and its relationships with organizational citizenship behavior and counterproductive work behavior. Pers Indiv Differ. 2017; 116: 144–56. doi: 10.1016/j.paid.2017.04.015

18. Sánchez-Álvarez N, Extremera N, Fernández-Berrocal P. The relation between emotional intelligence and subjective well-being: A meta-analytic investigation. J Posit Psychol. 2016; 11: 276–85. doi: 10.1080/17439760.2015.1058968

19. Schutte NS, Malouff JM, Thorsteinsson EB, Bhullar N, Rooke SE. A meta-analytic investigation of the relationship between emotional intelligence and health. Pers Indiv Differ. 2007; 42: 921–33. doi: 10.1016/j.paid.2006.09.003

20. Peña-Sarrionandia A, Mikolajczak M, Gross JJ. Integrating emotion regulation and emotional intelligence traditions: A meta-analysis. Front Psychol. 2015; 6: 160. doi: 10.3389/fpsyg.2015.00160 25759676

21. Sarrionandia A, Mikolajczak M. A meta-analysis of the possible behavioural and biological variables linking trait emotional intelligence to health. Health Psychol Rev. 2019: Advance online publication. doi: 10.1080/17437199.2019.1641423 31284846

22. Richardson M, Abraham C, Bond R. Psychological correlates of university students' academic performance: A systematic review and meta-analysis. Psychol Bull. 2012; 138: 353–87. doi: 10.1037/a0026838 22352812

23. Miao C, Humphrey RH, Qian S. A meta-analysis of emotional intelligence and work attitudes. J Occup Organ Psychol. 2017; 90: 177–202. doi: 10.1111/joop.12167

24. Miao C, Humphrey RH, Qian S. A meta-analysis of emotional intelligence effects on job satisfaction mediated by job resources, and a test of moderators. Pers Indiv Differ. 2017; 116: 281–8. doi: 10.1016/j.paid.2017.04.031

25. Malouff JM, Schutte NS, Thorsteinsson EB. Trait emotional intelligence and romantic relationship satisfaction: A meta-analysis. Am J Fam Ther. 2014; 42: 53–66. doi: 10.1080/01926187.2012.748549

26. Brackett M A., Rivers S E., Salovey P. Emotional intelligence: Implications for personal, social, academic, and workplace success. Soc Personal Psychol Compass. 2011; 5: 88–103. doi: 10.1111/j.1751-9004.2010.00334.x

27. Petrides KV, Mikolajczak M, Mavroveli S, Sanchez-Ruiz MJ, Furnham A, Perez-Gonzalez JC. Developments in trait emotional intelligence research. Emot Rev. 2016; 8: 335–41. doi: 10.1177/1754073916650493

28. Brasseur S, Gregoire J, Bourdu R, Mikolajczak M. The Profile of Emotional Competence (PEC): Development and validation of a self-reported measure that fits dimensions of emotional competence theory. PloS one. 2013; 8: e62635. doi: 10.1371/journal.pone.0062635 23671616

29. Parker JD, Michael Bagby R, Taylor GJ, Endler NS, Schmitz P. Factorial validity of the 20-item Toronto Alexithymia Scale. Eur J Personality. 1993; 7: 221–32. doi: 10.1002/per.2410070403

30. Mikolajczak M, Avalosse H, Vancorenland S, Verniest R, Callens M, van Broeck N, et al. A nationally representative study of emotional competence and health. Emotion. 2015; 15: 653–67. doi: 10.1037/emo0000034 25893449

31. Min MC, Takai J. The effect of emotional competence on relational quality: Comparing Japan and Myanmar. Curr Psychol. 2018: Advance online publication. doi: 10.1007/s12144-018-0002-9

32. Min MC, Islam MN, Wang L, Takai J. Cross-cultural comparison of university students’ emotional competence in Asia. Curr Psychol. 2018: Advance online publication. doi: 10.1007/s12144-018-9918-3

33. Batselé E, Stefaniak N, Fantini-Hauwel C. Resting heart rate variability moderates the relationship between trait emotional competencies and depression. Pers Indiv Differ. 2019; 138: 69–74. doi: 10.1016/j.paid.2018.09.020

34. Constant E, Christophe V, Bodenmann G, Nandrino J-L. Attachment orientation and relational intimacy: The mediating role of emotional competences. Curr Psychol. 2018: Advance online publication. doi: 10.1007/s12144-018-0062-x

35. Kotsou I, Leys C, Fossionc P. Acceptance alone is a better predictor of psychopathology and well-being than emotional competence, emotion regulation and mindfulness. J Affect Disorders. 2018; 226: 142–5. doi: 10.1016/j.jad.2017.09.047 28972931

36. Nozaki Y. Cross-cultural comparison of the association between trait emotional intelligence and emotion regulation in European-American and Japanese populations. Pers Indiv Differ. 2018; 130: 150–5. doi: 10.1016/j.paid.2018.04.013

37. Fantini-Hauwel C, Mikolajczak M. Factor structure, evolution, and predictive power of emotional competencies on physical and emotional health in the elderly. J Aging Health. 2014; 26: 993–1014. doi: 10.1177/0898264314535633 24920650

38. Schmitt TA, Sass DA, Chappelle W, Thompson W. Selecting the "Best" factor structure and moving measurement validation forward: An illustration. J Pers Assess. 2018; 100: 345–62. doi: 10.1080/00223891.2018.1449116 29630411

39. Howard JL, Gagné M, Morin AJS, Forest J. Using bifactor exploratory structural equation modeling to test for a continuum structure of motivation. J Manage. 2018; 44: 2638–64. doi: 10.1177/0149206316645653

40. Wu C-H, Chen LH. Examining dual meanings of items in 2 × 2 Achievement Goal Questionnaires through MTMM modeling and MDS approach. Educ Psychol Meas. 2009; 70: 305–22. doi: 10.1177/0013164409344501

41. Asparouhov T, Muthén B, Morin AJS. Bayesian structural equation modeling with cross-loadings and residual covariances: Comments on Stromeyer et al. J Manage. 2015; 41: 1561–77. doi: 10.1177/0149206315591075

42. Marsh HW, Bailey M. Confirmatory factor analyses of Multitrait-Multimethod data: A comparison of alternative models. Appl Psychol Meas. 1991; 15: 47–70. doi: 10.1177/014662169101500106

43. Hopwood CJ, Donnellan MB. How should the internal structure of personality inventories be evaluated? Pers Soc Psychol Rev. 2010; 14: 332–46. doi: 10.1177/1088868310361240 20435808

44. Costa PT Jr., McCrae RR. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO- FFI) professional manual. Odessa, FL: Psychological Assessment Resources; 1992.

45. Marsh HW, Muthén B, Asparouhov T, Lüdtke O, Robitzsch A, Morin AJS, et al. Exploratory structural equation modeling, integrating CFA and EFA: Application to students' evaluations of university teaching. Struct Equ Model. 2009; 16: 439–76. doi: 10.1080/10705510903008220

46. Muthén B, Asparouhov T. Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychol Methods. 2012; 17: 313–35. doi: 10.1037/a0026802 22962886

47. de Beer LT, Bianchi R. Confirmatory factor analysis of the Maslach Burnout Inventory: A Bayesian structural equation modeling approach. Eur J Psychol Assess. 2019; 35: 217–24. doi: 10.1027/1015-5759/a000392

48. Dombrowski SC, Golay P, McGill RJ, Canivez GL. Investigating the theoretical structure of the DAS-II core battery at school age using Bayesian structural equation modeling. Psychol Schools. 2018; 55: 190–207. doi: 10.1002/pits.22096

49. Fong TCT, Ho RTH. Factor analyses of the Hospital Anxiety and Depression Scale: A Bayesian structural equation modeling approach. Qual Life Res. 2013; 22: 2857–63. doi: 10.1007/s11136-013-0429-2 23670233

50. Fong TCT, Ho RTH. Dimensionality of the 9-item Utrecht Work Engagement Scale revisited: A Bayesian structural equation modeling approach. J Occup Health. 2015; 57: 353–8. doi: 10.1539/joh.15-0057-OA 25958976

51. Golay P, Reverte I, Rossier J, Favez N, Lecerf T. Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling. Psychol Assess. 2013; 25: 496–508. doi: 10.1037/a0030676 23148651

52. Reis D. Further insights into the German version of the Multidimensional Assessment of Interoceptive Awareness (MAIA): Exploratory and Bayesian structural equation modeling approaches. Eur J Psychol Assess. 2019; 35: 317–25. doi: 10.1027/1015-5759/a000404

53. Elasy TA, Gaddy G. Measuring subjective outcomes: Rethinking reliability and validity. J Gen Intern Med. 1998; 13: 757–61. doi: 10.1046/j.1525-1497.1998.00228.x 9824522

54. Golino HF, Epskamp S. Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PloS one. 2017; 12: e0174035. doi: 10.1371/journal.pone.0174035 28594839

55. Golino HF, Demetriou A. Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis. Intelligence. 2017; 62: 54–70. doi: 10.1016/j.intell.2017.02.007

56. Golino HF. EGA: Exploratory Graph Analysis: Estimating the number of dimensions in psychological data. R package version 0.4 ed2019.

57. R Core Team. R 3.5.0: A language and environment for statistical computing. R Foundation for Statistical Computing; 2018.

58. Muthén LK, Muthén BO. Mplus user's guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén; 1998–2017.

59. Li CH. Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behav Res Methods. 2016; 48: 936–49. doi: 10.3758/s13428-015-0619-7 26174714

60. Elliot AJ, Murayama K. On the measurement of achievement goals: Critique, illustration, and application. J Educ Psychol. 2008; 100: 613–28. doi: 10.1037/0022-0663.100.3.613

61. Brown TA. Confirmatory factor analysis for applied research. New York, NY: Guilford Press; 2006.

62. Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model. 1999; 6: 1–55. doi: 10.1080/10705519909540118

63. Asparouhov T, Muthén B. Prior-Posterior Predictive P-values (Mplus Web Notes: No. 22). 2017.

64. Hoijtink H, van de Schoot R. Testing small variance priors using prior-posterior predictive p values. Psychol Methods. 2018; 23: 561–9. doi: 10.1037/met0000131 28368177

65. Lance CE, Fan Y. Convergence, admissibility, and fit of alternative confirmatory factor analysis models for MTMM data. Educ Psychol Meas. 2015; 76: 487–507. doi: 10.1177/0013164415601884 29795875

66. Rindskopf D. Parameterizing inequality constraints on unique variances in linear structural models. Psychometrika. 1983; 48: 73–83. doi: 10.1007/BF02314677

67. Depaoli S, van de Schoot R. Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist. Psychol Methods. 2017; 22: 240–61. doi: 10.1037/met0000065 26690773

68. Reeck C, Ames DR, Ochsner KN. The social regulation of emotion: An integrative, cross-disciplinary model. Trends Cogn Sci. 2016; 20: 47–63. doi: 10.1016/j.tics.2015.09.003 26564248

69. Zaki J, Williams WC. Interpersonal emotion regulation. Emotion. 2013; 13: 803–10. doi: 10.1037/a0033839 24098929

70. Nozaki Y, Mikolajczak M. Extrinsic emotion regulation. Emotion. 2019: Advance online publication. doi: 10.1037/emo0000636

71. Leary MR, Raimi KT, Jongman-Sereno KP, Diebels KJ. Distinguishing intrapsychic from interpersonal motives in psychological theory and research. Perspect Psychol Sci. 2015; 10: 497–517. doi: 10.1177/1745691615583132 26177950

72. Pekaar KA, Bakker AB, Born MP, van der Linden D. The consequences of self- and other-focused emotional intelligence: Not all sunshine and roses. J Occup Health Psychol. 2019; 24: 450–66. doi: 10.1037/ocp0000134 30284844

73. Nozaki Y, Koyasu M. The relationship between trait emotional intelligence and interaction with ostracized others' retaliation. PloS one. 2013; 8: e77579. doi: 10.1371/journal.pone.0077579 24194890

74. Pérez-González JC, Qualter P. Emotional intelligence and emotional education in the school years. In: Dacre Pool L, Qualter P, editors. An introduction to emotional intelligence. England: John Wiley & Sons; 2018. p. 81–104.

75. Kotsou I, Mikolajczak M, Heeren A, Grégoire J, Leys C. Improving emotional intelligence: A systematic review of existing work and future challenges. Emot Rev. 2019; 11: 151–65. doi: 10.1177/1754073917735902

76. Niven K, Totterdell P, Stride CB, Holman D. Emotion regulation of others and self (EROS): The development and validation of a new individual difference measure. Curr Psychol. 2011; 30: 53–73. doi: 10.1007/s12144-011-9099-9

77. Joseph DL, Newman DA. Emotional intelligence: An integrative meta-analysis and cascading model. J Appl Psychol. 2010; 95: 54–78. doi: 10.1037/a0017286 20085406

78. Palmer BR, Gignac G, Manocha R, Stough C. A psychometric evaluation of the Mayer–Salovey–Caruso Emotional Intelligence Test Version 2.0. Intelligence. 2005; 33: 285–305. doi: 10.1016/j.intell.2004.11.003

79. Mayer JD, Caruso DR, Salovey P. The ability model of emotional intelligence: Principles and updates. Emot Rev. 2016; 8: 290–300. doi: 10.1177/1754073916639667

80. Elfenbein HA, Barsade SG, Eisenkraft N. The social perception of emotional abilities: Expanding what we know about observer ratings of emotional intelligence. Emotion. 2015; 15: 17–34. doi: 10.1037/a0038436 25664949

81. Flake JK, Pek J, Hehman E. Construct validation in social and personality research: Current practice and recommendations. Soc Psychol Pers Sci. 2017; 8: 370–8. doi: 10.1177/1948550617693063


Článek vyšel v časopise

PLOS One


2019 Číslo 11