Measurement Invariance of the Appearance Schemas Inventory–Revised and the Body Image Quality of Life Inventory Across Age and Gender
Bibliographic record
Abstract
The majority of body image measures have largely been developed with younger female samples. Before these measures can be applied to men, and to middle-aged and older women, and used to make gender and age comparisons, they must exhibit adequate cross-group measurement invariance. This study examined the age and gender cross-group measurement invariance of the Appearance Schemas Inventory-Revised (ASI-R) and the Body Image Quality of Life Inventory (BIQLI), with a sample of 1,262 adults (422 men and 840 women) aged 18 to 98 years. For the ASI-R, all groups met requirements for configural and metric invariance. Scalar invariance was found only for the three age groups, which indicated that mean comparisons may be conducted across gender for young, middle-aged, and older adults but should not be conducted across age groups within either gender. Results for the BIQLI indicated that observed mean comparisons may be conducted across all age and gender groups.
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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 | 0.006 | 0.018 |
| 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 | 0.000 | 0.001 |
| 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".