Analyzing the Factor Structure of the Toronto Empathy Questionnaire: Dimensionality, Reliability, Validity, Measurement Invariance and One-Year Stability of the German Version
Bibliographic record
Abstract
(TEQ; Spreng et al., Journal of Personality Assessment, 91(1), 62-71 (2009)) was developed as a brief unidimensional tool by statistically forming a consensus from existing measures of the construct. The present study aimed to (1) validate a German version of the TEQ, and (2) contribute empirical evidence to the ongoing debate regarding a singular versus multidimensional factor structure of the TEQ. One cross-sectional and two longitudinal studies were performed, with a total of 1,075 participants. Our initial exploratory factor analyses suggested either a one- or a two-factor structure (with the two-factors clustering straight and reverse-scored items); the two-factor model outperformed the one-factor model using confirmatory factor analyses. However, after negated items were replaced by positively reworded alternatives, both models fit the data equally well. A comparison of the correlation patterns with numerous external measures indicated that a second factor of the TEQ is a methodological artifact of item wording. Finally, a unidimensional TEQ scale showed sufficient internal consistency, two-week test-retest reliability, one-year stability, as well as convergent and discriminant validity with measures of empathy, emotion recognition, emotion regulation, altruism, social desirability, and the Big Five personality traits.
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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 | 0.006 | 0.011 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".