Advanced search
1 file | 3.36 MB Add to list

Assessing emotional intelligence abilities, acquiescent and extreme responding in situational judgment tests using principal component metrics

Author
Organization
Abstract
Principal Component Metrics is a novel theoretically-based and data-driven methodology that enables the evaluation of the internal structure at item level of maximum emotional intelligence tests. This method disentangles interindividual differences in emotional ability from acquiescent and extreme responding. Principal Component Metrics are applied to existing (Mayer-Salovey-Caruso Emotional Intelligence Test) and assembled (specifically, the Situational Test of Emotion Understanding, the Situational Test of Emotion Management, and the Geneva Emotion Recognition Test) emotional intelligence test batteries in an analysis of three samples (total <jats:italic>N</jats:italic> = 2,303 participants). In undertaking these analyses important aspects of the nomological network of emotional intelligence, acquiescent, and extreme responding are investigated. The current study adds a central piece of empirical validity evidence to the emotional intelligence domain. In the three different samples, theoretically predicted internal structures at item level were found using raw item scores. The validity of the indicators for emotional intelligence, acquiescent, and extreme responding was confirmed by their relationships across emotional intelligence tests and by their nomological networks. The current findings contribute to evaluating the efficacy of the emotional intelligence construct as well as the validity evidence surrounding the instruments that are currently designed for its assessment, in the process opening new perspectives for analyzing existing and constructing new emotional intelligence tests.
Keywords
General Psychology

Downloads

  • Publisher version.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 3.36 MB

Citation

Please use this url to cite or link to this publication:

MLA
Fontaine, Johnny, et al. “Assessing Emotional Intelligence Abilities, Acquiescent and Extreme Responding in Situational Judgment Tests Using Principal Component Metrics.” FRONTIERS IN PSYCHOLOGY, vol. 13, 2022, doi:10.3389/fpsyg.2022.813540.
APA
Fontaine, J., Sekwena, E., Veirman, E., Schlegel, K., MacCann, C., Roberts, R. D., & Scherer, K. R. (2022). Assessing emotional intelligence abilities, acquiescent and extreme responding in situational judgment tests using principal component metrics. FRONTIERS IN PSYCHOLOGY, 13. https://doi.org/10.3389/fpsyg.2022.813540
Chicago author-date
Fontaine, Johnny, Eva Sekwena, Elke Veirman, Katja Schlegel, Carolyn MacCann, Richard D. Roberts, and Klaus R. Scherer. 2022. “Assessing Emotional Intelligence Abilities, Acquiescent and Extreme Responding in Situational Judgment Tests Using Principal Component Metrics.” FRONTIERS IN PSYCHOLOGY 13. https://doi.org/10.3389/fpsyg.2022.813540.
Chicago author-date (all authors)
Fontaine, Johnny, Eva Sekwena, Elke Veirman, Katja Schlegel, Carolyn MacCann, Richard D. Roberts, and Klaus R. Scherer. 2022. “Assessing Emotional Intelligence Abilities, Acquiescent and Extreme Responding in Situational Judgment Tests Using Principal Component Metrics.” FRONTIERS IN PSYCHOLOGY 13. doi:10.3389/fpsyg.2022.813540.
Vancouver
1.
Fontaine J, Sekwena E, Veirman E, Schlegel K, MacCann C, Roberts RD, et al. Assessing emotional intelligence abilities, acquiescent and extreme responding in situational judgment tests using principal component metrics. FRONTIERS IN PSYCHOLOGY. 2022;13.
IEEE
[1]
J. Fontaine et al., “Assessing emotional intelligence abilities, acquiescent and extreme responding in situational judgment tests using principal component metrics,” FRONTIERS IN PSYCHOLOGY, vol. 13, 2022.
@article{8753003,
  abstract     = {{Principal Component Metrics is a novel theoretically-based and data-driven methodology that enables the evaluation of the internal structure at item level of maximum emotional intelligence tests. This method disentangles interindividual differences in emotional ability from acquiescent and extreme responding. Principal Component Metrics are applied to existing (Mayer-Salovey-Caruso Emotional Intelligence Test) and assembled (specifically, the Situational Test of Emotion Understanding, the Situational Test of Emotion Management, and the Geneva Emotion Recognition Test) emotional intelligence test batteries in an analysis of three samples (total <jats:italic>N</jats:italic> = 2,303 participants). In undertaking these analyses important aspects of the nomological network of emotional intelligence, acquiescent, and extreme responding are investigated. The current study adds a central piece of empirical validity evidence to the emotional intelligence domain. In the three different samples, theoretically predicted internal structures at item level were found using raw item scores. The validity of the indicators for emotional intelligence, acquiescent, and extreme responding was confirmed by their relationships across emotional intelligence tests and by their nomological networks. The current findings contribute to evaluating the efficacy of the emotional intelligence construct as well as the validity evidence surrounding the instruments that are currently designed for its assessment, in the process opening new perspectives for analyzing existing and constructing new emotional intelligence tests.}},
  articleno    = {{813540}},
  author       = {{Fontaine, Johnny and Sekwena, Eva and Veirman, Elke and Schlegel, Katja and MacCann, Carolyn and Roberts, Richard D. and Scherer, Klaus R.}},
  issn         = {{1664-1078}},
  journal      = {{FRONTIERS IN PSYCHOLOGY}},
  keywords     = {{General Psychology}},
  language     = {{eng}},
  pages        = {{17}},
  title        = {{Assessing emotional intelligence abilities, acquiescent and extreme responding in situational judgment tests using principal component metrics}},
  url          = {{http://dx.doi.org/10.3389/fpsyg.2022.813540}},
  volume       = {{13}},
  year         = {{2022}},
}

Altmetric
View in Altmetric