Psychometric properties of the Taiwan version of Emotion Dysregulation Inventory in Autism Spectrum Disorder
Bibliographic record
Abstract
BACKGROUND: While the Emotion Dysregulation Inventory (EDI) for autistic people has been validated in many Western countries, its psychometric properties have remained unclear in East Asia. METHODS: We translated the EDI into traditional Chinese and evaluated its psychometric properties among autistic children and youth in Taiwan. We enrolled 200 participants (182 male/18 female) aged 7-30 years from five clinical trials and conducted secondary analyses, assessing internal consistency reliability, confirmatory factor analysis, and convergent validity. RESULTS: Our results showed that the Taiwan version of the EDI had strong internal consistency (Cronbach's alpha are 0.978 and 0.864 for each factor). Confirmatory factor analysis demonstrated acceptable fit of two-factors structure. The Taiwan version of EDI showed good convergent validity with established measurements including the Aberrant Behavior Checklist-Irritability subscale and Child Behavior Checklist-Dysregulation Profile. CONCLUSION: Our findings support the Taiwan version of EDI is a reliable and potentially valid instrument for assessing emotion dysregulation in autistic children and youth in Taiwan.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| 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".