When and for whom are relative age effects important? evidence from a simple test of cardiorespiratory fitness
Bibliographic record
Abstract
OBJECTIVES: When individuals of different ages are combined into a single group and an ability that varies with age is measured, younger individuals are disadvantaged. This phenomenon is known as a relative age effect (RAE) and has been shown to be widespread in sport and education. METHODS: In this article, we examine RAEs in a large group of children tested repeatedly on the 20-m shuttle run, a common test of cardiorespiratory fitness. Following up on an earlier study that measured change with age, we add a growth curve model for change in variance, which makes it possible to derive RAEs for individuals of different ages and ability levels. RESULTS: Results show that a 1-year difference in age is associated with a change in performance of about 0.2 standard deviations. For 1-year age groups, this gives rise to ranking errors of 4 percentile ranks or less. We also show, however, that these relatively small ranking errors are capable of producing large age differences within groups identified as exceptional. Depending on the level of ability required for selection, children born in the first quarter of the year can be expected to outnumber those born in the last by 1.5 times, 2 times, or more. This finding is consistent with previously reported variation in RAEs at different performance levels. CONCLUSIONS: Results imply that RAEs are likely to be of relatively minor concern when people are graded or ranked but can produce substantial inequities and misclassifications when people with extremely high or extremely low ability levels undergo selection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".