Comparisons and Conversions: A Methodological Note and Caution for Meta-Analysis in Sport and Exercise Psychology
Bibliographic record
Abstract
Meta-analysis is a powerful tool in sport and exercise psychology. However, it has a number of pitfalls, and some lead to ill-advised comparisons and overestimation of effects. The impetus for this research note is provided by a recent systematic review of meta-analyses that examined the correlates of sport performance and has fallen foul of some of the pitfalls. Although the systematic review potentially has great value for researchers and practitioners alike, it treats effects from correlational and intervention studies as yielding equivalent information, double-counts multiple studies, and uses an effect size for correlational studies (Cohen's d) that provides an extreme contrast of unclear practical relevance. These issues impact interpretability, bias, and usefulness of the findings. This methodological note explains each pitfall and illustrates use of an appropriate equivalent effect size for correlational studies (Mathur and VanderWeele's d) to help researchers avoid similar issues in future work.
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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 | 0.634 | 0.791 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.008 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 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".