Analyzing the Factor Structure of the Toronto Empathy Questionnaire: Dimensionality, Reliability, Validity, Measurement Invariance and One-Year Stability of the German Version
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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