Translation and validation of a Chinese version of the Appearance Schemas Inventory-Revised in Chinese adults
Bibliographic record
Abstract
The current study translated the Appearance Schemas Inventory-Revised (ASI-R) into Chinese (Mandarin) and examined its psychometric properties in Chinese adult women and men. Sample 1 included 400 women and 400 men to examine the factor structure of the ASI-R with exploratory factor analysis (EFA). Sample 2 involved 300 women and 300 men, and the EFA-derived factor structures in Sample 1 of the ASI-R were examined with exploratory structural equation modeling (ESEM), bifactor ESEM (B-ESEM), and bifactor ESEM with correlated uniqueness for negatively worded items (B-ESEM-CU) for both women and men. Results of the EFA identified a 4-factor model in women and a 2-factor model in men. The B-ESEM-CU consistently showed the best model fit. In the B-ESEM-CU, the general factor was well-defined, but the specific factors were not, supporting the use of the global factor to conceptualize the ASI-R for Chinese women and men. Evidence of adequate internal consistency, test-retest reliability, and construct validity of the global factor of the ASI-R was suggested in both women and men. Findings suggest the ASI-R is a useful instrument to measure body image investment in Chinese women and men, specifically using the B-ESEM-CU to understand the dimensionality of the ASI-R.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".