Turkish Validity and Reliability Study of the Emotion Regulation Questionnaire-Short Form
Bibliographic record
Abstract
Objective: This study aimed to adapt the Emotion Regulation Questionnaire-Short Form (ERQ-SF) to Turkish, and to investigate its psychometric properties. Method: A total of 325 adults (255 females (78.5%) and 70 males (21.5%) between the ages of 18-58 (27.86±8.37), consisting of university students and community samples, participated in the study. To evaluate the test-retest reliability, 41 university students were administered the DDA-KF at five-week intervals, and for criterion-related validity, the DDA-KF, Emotion Regulation Questionnaire, Difficulties in Emotion Regulation Questionnaire, Perth Alexithymia Scale and Depression, Anxiety and Stress Scale were administered to a community sample of 165 people, 142 (86.1%) women and 23 (13.9%) men, including university students. Results: The Cronbach's alpha coefficients of the scale were .71 for the suppression dimension and .73 for the cognitive reappraisal dimension. According to the findings of the exploratory factor analysis, the scale was suitable for factor analysis and the scale items had high factor loadings ranging from .64 to .89, and the confirmatory factor analysis showed that the fit indices of the two-factor model were good (X2/df =1.39, CFI=.98, TLI=.96, GFI=.97, AGFI=.94) in parallel with the original study. Conclusion: The ERQ-SF demonstrates strong psychometric properties, making it a valid and reliable tool for assessing emotion regulation in Turkish samples for research purposes. Additionally, its concise six-item format ensures ease of administration.
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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 | 0.004 | 0.010 |
| 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 | 0.001 | 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".