Comparing Emotional Intelligence Assessment Tools: Predictive Validity Of Self-Report And Ability-Based Measures In Workplace Performance
Bibliographic record
Abstract
In the past few years, emotional intelligence (Mayer and Salovey,1993) has gained recognition as a central concept in organizational psychology, influencing workplace performance, leadership, and employee well-being. Despite its significance, the field remains divided regarding the most effective method of assessing EI. This study critically compares self-report measures like Self-Report Emotional Intelligence Test (SREIT; Schutte et al., 1998) and Emotional Quotient Inventory 2.0(Bar-On, R. 2004) with ability-based assessments, represented by the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) by J. D. Mayer, P. Salovey, and D. R. Caruso, 2002, Toronto, Ontario: Multi-Health Systems, Inc. to examine their predictive validity in workplace contexts. Drawing on recent organizational studies, the paper evaluates the strengths and limitations of both approaches, with particular attention to self-perception bias in self-reports and task-based validity in ability tests. A comparative framework is developed to assess predictive power across performance outcomes, including job effectiveness, teamwork, conflict resolution, and leadership competence. Findings suggest that while self-report measures(capture subjective awareness and self-concept, ability-based tools more consistently predict observable behaviors and workplace outcomes. The discussion underscores the importance of integrative assessment strategies that combine the accessibility of self-reports with the objectivity of ability-based measures. It has implications for management for talent, leadership development, and evaluation of employees are outlined, along with limitations in methodology and directions for research in the future. This study contributes to advancing a more refined and accurate understanding of EI measurement and its organizational applications.
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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 | 0.024 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".