Psychometric properties of the Emotion Regulation Questionnaire in a Mexican sample and their correlation with empathy and alexithymia
Bibliographic record
Abstract
The Emotion Regulation Questionnaire (ERQ) measures the use of two emotional regulation strategies, cognitive reappraisal, and expressive suppression. Although widely used, there is no description of the psychometric properties of the ERQ and its correlations with alexithymia and empathy in a Mexican sample. We examine the psychometric properties of the ERQ in a Mexican sample (N = 792) assessing its correlations with alexithymia and empathy utilizing the Toronto Alexithymia Scale and the Interpersonal Reactivity Index. Confirmatory factor analyses confirmed 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 cognitive empathy scales and empathic concern. The ERQ has strong psychometric properties in a Mexican sample and can be applied in a confident manner in conjunction with other tests to complement the assessment of affective traits. In addition, considering previous suggestions regarding the relation between emotion regulation strategies and different components of the empathic response, the correlations between empathy measures and the emotional regulation strategies shown in this study opens a pathway to further research such interactions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".