Faking emotional intelligence (EI): comparing response distortion on ability and trait‐based EI measures
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We compared the susceptibility of two emotional intelligence (EI) tests to faking. In a laboratory study using a within‐subjects design, participants completed the EQ‐i and the MSCEIT in two sessions. In the first session (i.e., the ‘applicant condition’), participants were given a job description and asked to respond to the EI measures as though they were applying for that job. Participants returned 2 weeks later to repeat the tests in a ‘non‐applicant’ condition in which they were told to answer as honestly as possible. Mean differences between conditions indicated that the EQ‐i was more susceptible to faking than the MSCEIT. Faking indices predicted applicant condition EQ‐i scores, after controlling for participants' non‐applicant EQ‐i scores, whereas the faking indices were unrelated to applicant condition MSCEIT scores, when the non‐applicant MSCEIT scores were controlled. Using top‐down selection, participants were more likely to be selected based on their applicant condition EQ‐i scores than their non‐applicant EQ‐i scores, but they had an equal likelihood of being selected based on their MSCEIT scores from each condition. Implications for the use of these two EI tests are discussed. Copyright © 2007 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it