Evaluating emotion regulation ability across negative and positive emotions: psychometric properties of the Perth Emotion Regulation Competency Inventory (PERCI) in American adults and Iranian adults and adolescents
Bibliographic record
Abstract
Objective A critical factor for adaptive psychological functioning is the ability to successfully regulate negative and positive emotions. Various tools and methods have been developed to assess emotion regulation competence. Recently, the Perth Emotion Regulation Competency Inventory (PERCI) was developed to overcome some of the limitations of previous assessment tools including a lack of emotion regulation assessment across both positive and negative emotions. To date, no studies have examined the PERCI’s psychometric properties among adolescents and non-Western general populations.Method To address this gap in the literature, we examined the psychometric properties of the PERCI among Iranian adolescents (n = 557), Iranian adults (n = 926), and American adults (n = 242). Participants also completed Emotion Regulation Questionnaire (ERQ), Toronto Alexithymia Scale-20 (TAS-20), and Depression Anxiety and Stress Scale-21 (DASS-21) for measuring the concurrent validity of the PERCI.Results Confirmatory factor analyses supported the intended eight-factor structure that distinguishes between different emotion regulation components and negative and positive emotions. The eight-factor structure was also found invariant in terms of gender, age, and culture groups. Furthermore, the PERCI demonstrated good internal consistency, test-retest reliability, as well as expected associations with measures of psychopathology, emotion regulation strategy, and alexithymia.Conclusions Our findings indicate that the PERCI has strong psychometric properties among both Middle Eastern and Western samples and can also be utilised with adolescents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".