Toward a brief multidimensional assessment of emotional intelligence: Psychometric properties of the Emotional Quotient Inventory—Short Form.
Bibliographic record
Abstract
Although several brief instruments are available for the emotional intelligence (EI) construct, their conceptual coverage tends to be quite limited. One notable exception is the short form of the Emotional Quotient Inventory (EQ-i:S), which measures multiple EI dimensions in addition to a global EI index. Despite the unique advantage offered by the inventory, psychometric properties of the EQ-i:S scores have not yet been systematically evaluated. Such an evaluation was the main goal of the present investigation. Using data from 2,508 undergraduates, the authors conducted 2 studies involving factor structure, internal reliability, 6-month temporal stability, and construct validity of the EQ-i:S responses, both for the total EQ scale and for each constituent dimension. The results supported the multidimensional measurement structure of the EQ-i:S, with each dimension producing internally consistent, temporally stable, and theoretically meaningful responses. Scores on the EQ-i:S were associated more strongly with performance on an ability test of EI and with a conceptually similar construct of alexithymia than with the broader dimensions of basic personality and explained nontrivial amounts of incremental variance in the criterion symptoms of attention deficit/hyperactivity disorder. Moreover, scores on each EQ-i:S dimension exhibited unique patterns of associations with the validation variables. The discussion highlights the advantages of the multidimensional approach in the assessment and study of EI.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".