Structural and Predictive Properties of the Emotional Quotient Inventory Youth Version–Short Form (EQ-i:YV[S])
Bibliographic record
Abstract
Emotional intelligence (EI) is a popular construct with concentrated areas of application in education and health contexts. There is a need for reliable and valid measurement of EI in young people, with brief yet sensitive measures of the construct preferable for use in time-limited settings. However, the proliferation of EI measures has often outpaced rigorous psychometric evaluation (Gignac, 2009 Gignac, G. E. (2009). Psychometrics and the measurement of emotional intelligence. In C. Stough, D. H. Saklofske, & J. D. A. Parker (Eds.), Assessing emotional intelligence: Theory, research and applications (pp. 9–40). New York, NY: Springer.[Crossref] , [Google Scholar]). Using data from 849 adolescents (407 females, 422 males) aged 11 to 16 years (M age 13.4, SD = 1.2 years), this article systematically examines the structural and predictive properties of a frequently employed measure of adolescent trait EI—the Emotional Quotient Inventory Youth Version–Short Form (EQ-i:YV[S]); Bar-On & Parker, 2000 Bar-On, R., & Parker, J. D. (2000). BarOn Emotional Quotient Inventory: Youth Version—Technical manual. Toronto, Canada: Multi-Health Systems. [Google Scholar]). Although the intended multidimensional factor structure was recovered through confirmatory factor analysis, the statistical and conceptual coherency of the underlying model was inadequate. Using a multitrait–multimethod approach, the EQ-i:YV(S) was found to converge with other measures of EI; however, evidence for divergent validity (Big Five personality dimensions) was less robust. Predictive utility for adolescent mental health outcomes (depression, disruptive behavior) was also limited. Findings suggest that use of the EQ-i:YV(S) for predictive or evaluative purposes should be avoided until refinements to the scale are made.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".