Psychometric analyses of the general mattering scale, anti-mattering scale, and the fear of not mattering inventory in Chinese youth
Bibliographic record
Abstract
The importance of mattering for children and adolescents has garnered increasing attention. However, systematic psychometric examination for the related scales is lacking in this population. To address this gap, the present study employed classical test theory and Rasch analysis to evaluate the reliability and validity of the General Mattering Scale (GMS), Anti-Mattering Scale (AMS), and Fear of Not Mattering Inventory (FNMI) in a sample of 4,225 Chinese children and adolescents from 16 schools spanning primary, middle, and senior high school levels. The scales exhibited high reliability and validity, with Rasch analysis confirming unidimensionality for each, although one of the five GMS items showed poor fit for primary and senior high school samples. Multiple-group confirmatory factor analysis demonstrated strong measurement invariance for the AMS and FNMI across age groups, but not for the GMS. Scores on the GMS, AMS, and FNMI each accounted for a significant and unique portion of variance in depression, anxiety, and stress, underscoring their incremental validity. The AMS and FNMI are well-suited for assessing anti-mattering and fear of not mattering across Chinese youth populations, while the GMS requires refinement due to Item 1's poor fit and lack of cross-group invariance. Notably, anti-mattering showed the strongest associations with depression, anxiety, and stress across all age groups, highlighting its particular importance for youth mental health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".