Replication Data for: Psychometric properties of the Emotion Regulation Questionnaire
Bibliographic record
Abstract
The Emotion Regulation Questionnaire (ERQ) measures two emotional regulation strategies, cognitive reappraisal and expressive suppression. Although widely used, there is no much information about the way both strategies relate to alexithymia and empathy, in addition the psychometric properties of the ERQ in a Mexican sample are unknown. We examined such psychometric properties in a Mexican sample (N = 792), characterizing also the way both strategies relate with alexithymia and empathy utilizing the Toronto Alexithymia Scale and the Interpersonal Reactivity Index. Confirmatory factor analyses corroborated the two-factor model. Each factor showed acceptable levels of Cronbach’s alpha reliability scores. Cognitive reappraisal scores correlated negatively with alexithymia and positively with higher empathy measures, while expressive suppression correlated positively with alexithymia and personal distress, and negatively with perspective taking and empathic concern. Although, both strategies correlated with most of the alexithymia and empathy subscales, the strength of these correlations was different, cognitive reappraisal correlated more strongly with both cognitive empathy scales and with empathic concern; whereas expressive suppression showed stronger correlations with personal distress and all the alexithymia scales. Furthermore, the relation between cognitive reappraisal and perspective taking seems to be moderated by empathic concern. Our findings suggest that the ERQ has strong psychometric properties in a Mexican sample and its use in conjunction with other tests can complement the assessment of affective 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.
How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".