Statistical Validity of Using Ratio Variables in Human Kinetics Research
Bibliographic record
Abstract
The purposes of this study were to investigate the validity of the simple ratio and three alternative deflation models and examine how the variation of the numerator and denominator variables affects the reliability of a ratio variable. A simple ratio and three alternative deflation models were fitted to four empirical data sets, and common criteria were applied to determine the best model for deflation. Intraclass correlation was used to examine the component effect on the reliability of a ratio variable. The results indicate that the validity, of a deflation model depends on the statistical characteristics of the particular component variables used, and an optimal deflation model for all ratio variables may not exist. Therefore, it is recommended that different models be fitted to each empirical data set to determine the best deflation model. It was found that the reliability of a simple ratio is affected by the coefficients of variation and the within- and between-trial correlations between the numerator and denominator variables. It was recommended that researchers should compute the reliability of the derived ratio scores and not assume that strong reliabilities in the numerator and denominator measures automatically lead to high reliability in the ratio measures.
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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 | 0.349 | 0.711 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".