The Psychometric Assessment of Empathy: Development and Validation of the Perth Empathy Scale
Bibliographic record
Abstract
Empathy, the ability to infer and share others’ affective states, plays a vital role in social interactions. However, no existing scale comprehensively assesses empathy’s cognitive and affective components across positive and negative emotional valence domains. This article explores the latent structure of the empathy construct and attempts to remedy past measurement limitations by developing and validating a new 20-item self-report measure, the Perth Empathy Scale (PES). In Study 1 ( N = 316), factor analyses revealed a coherent empathy construct comprised of cognitive and valence-specific affective components. Study 2 ( N = 331) replicated this factor structure, showed measurement invariance between males and females, and highlighted the importance of assessing negative and positive emotions in empathy. The PES showed convergent and discriminant validity from comparisons with alexithymia and other empathy measures. Overall, this article empirically establishes a conceptually clear structure of the multidimensional empathy construct, which the PES reliably and validly measures.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".