Construct Validity of the Fear of Negative Appearance Evaluation Scale in a Community Sample of French Adolescents
Bibliographic record
Abstract
The main objective of the present series of studies was to test the construct validity (i.e., content, factorial, and convergent validities) of the Fear of Negative Appearance Evaluation Scale (FNAES) in a community sample of French adolescents. A total sample of 683 adolescents was involved in three studies. The factorial validity and the measurement invariance of the FNAES were verified through a series of confirmatory factor analyses. The convergent validity of the FNAES was then verified through correlational analyses. The first study showed that the content and formulation of the French FNAES items were adequate for children and adolescents. The following two studies (Studies 2 to 3) provided (a) support for the factor validity, reliability, and convergent validity of a five-item French version of the FNAES, and (b) partial support for the measurement invariance of the resulting FNAES across genders. However, the latent mean structure of the FNAES did not prove to be invariant across genders, revealing a significantly higher latent mean score of FNAES in girls relative to boys. The present results, thus, provide preliminary evidence regarding the construct validity of the FNAES in a community sample of French adolescents. Recommendations for future practice and research regarding this instrument are outlined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".