Emotional Intelligence Tests: Potential Impacts on the Hiring Process for Accounting Students
Bibliographic record
Abstract
Emotional intelligence is increasingly recognized as being important for professional career success. Skills related to emotional intelligence (e.g. organizational commitment, public speaking, teamwork, and leadership) are considered essential. Human resource professionals have begun including tests of emotional intelligence (EI) in job applicant screening processes. Consequently, if accounting education fails to develop EI skills, students may seem to recruiters to be less qualified. Alternatively, if the tests for EI are inaccurate or easily manipulated, qualified applicants may be overlooked. We examine the ability of subjects studying accounting at a Canadian university to purposely alter their results on two of the most frequently used EI tests: the Emotional Quotient Inventory (EQ-i) and the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). We find that subjects can purposely change their EI score to fit the job description. We conclude that neither instrument is clearly better than the other is in the hiring process and both require revision as potential applicants are able purposely to alter their scores.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".