Measuring emotional intelligence with the MSCEIT 2: theory, rationale, and initial findings
Bibliographic record
Abstract
Introduction The model of emotional intelligence as an ability has evolved since its introduction 35 years ago. The revised model includes that emotional intelligence (EI) is a broad intelligence within the Cattell-Horn-Carroll (CHC) model of intelligence, and that more areas of problem solving are involved than originally detailed. An argument is made here that veridical scoring of EI test responses is a sound procedure relative to scoring keys based on expert consensus or a single emotion theory. To the degree that EI fits present-day theories of intelligence (i.e., the CHC model), any subsidiary factors of EI reasoning should correlate highly with one another. These and other considerations led to a revision of the original Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) to the MSCEIT 2. Methods The MSCEIT 2 was developed and tested across 5 studies: Two preliminary studies concerned, first, the viability of new item sets (Study 1, N = 43) and, in Study 2 (N = 8), the development of a veridical scoring key for each test item with the assistance of Ph.D. area experts. Next, a pilot study (Study 3, N = 523) and a normative study (Study 4, N = 3,000) each focused on the test’s item performance and factor structure, including whether a four-domain model continued to fit the data in a manner consistent with a cohesive broad intelligence. Study 5 (N = 221) examined the relation between the original and revised tests. Results The studies provide evidence for factor-supported subscale scores, and good reliability at the overall test level, with acceptable reliabilities for 3 of the 4 subscale scores, and adequate measurement precision across the range of most test-takers’ abilities. Discussion Overall, the MSCEIT 2 used updated theory to guide its construction and development. Its test scores fit the CHC model, and correlate with the original MSCEIT. The revised test is 33% shorter than the original.
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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 | 0.035 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".