Measurement Properties of Canadian Agility and Movement Skill Assessment for Children Aged 9–12 Years Using Rasch Analysis
Bibliographic record
Abstract
The Canadian Agility and Movement Skill Assessment (CAMSA) was recently widely used to assess fundamental motor skills in children. Although the CAMSA is reported to be reliable and valid, its measurement properties are not clear. This study aimed to examine the measurement properties of the CAMSA in a sample of Chinese children using Rasch analysis. The study sample was from 1,094 children aged 9-12 years in Zunyi City, Guizhou Province. Descriptive data were analyzed using SPSS 24.0 software, and the dichotomous data were analyzed by Winsteps version 4.5.4 and Facets 3.67.1 software performing Rasch analysis. The present study investigated CAMSA measurement characteristics by Rasch analysis, including the reliability of the rating instrument, unidimensionality, item-fit statistics, and differential item functioning (DIF). Inter-rater reliability and retest reliability showed that the CAMSA had a good internal consistency. Rasch analysis indicated that the CAMSA was unidimensional, locally independent, and had a good item-fit-statistic. Additionally, the CAMSA displayed a good fit for the item separation index (12.50 > 2.0), as well as for item reliability (0.99 > 0.90). However, the item difficulty of the CAMSA did not fit well with personal ability, and a significant DIF was found across genders. In the Chinese children sample test, the CAMSA demonstrated appropriate goodness-of-fit validity and rater reliability. Thus, future research will explore item difficulty and person ability fit, as well as DIF across genders.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".