Assessment of Trait Empathy Scales: Comparing the IRI and the TEQ on Convergent Validity with Sexual Prejudice, Gender Effects, and Temporal Stability
Bibliographic record
Abstract
The Interpersonal Reactivity Index (IRI) is a widely used trait measure of empathy which suffers from low convergent validity. The Toronto Empathy Questionnaire (TEQ) shows improved convergent validity but lacks assessment of test-retest reliability and clarity as to the construct(s) it measures. We compared the TEQ and IRI convergent validities (N = 2693) with trait sexual prejudice, a construct opposed to empathy, using the Modern Homonegativity Scale (MHS). The TEQ outperformed the IRI in terms of convergent validity with MHS. The IRI empathic concern subscale was most predictive of both TEQ and MHS scores. However, its perspective-taking subscale predicted the TEQ but not MHS scores. Results support the view that the IRI fantasy and personal distress subscales are not empathic constructs. The TEQ increased predictive power for every model it was added to, suggesting another construct might be at play beyond empathic concern and perspective-taking. Participant gender predicted TEQ and MHS scores but did not moderate their association. However, longitudinal invariance analysis (N = 568) revealed mixed findings concerning all the scales, with TEQ possibly tapping into different constructs across time. Implications for research on empathy and its association with sexual prejudice are discussed.
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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 | 0.005 | 0.017 |
| 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 | 0.000 |
| Open science | 0.000 | 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".