Reporting transparency in analysis of variance assumption testing
Bibliographic record
Abstract
ANOVA models in general are valid only insofar as both homoscedasticity and multiple comparisons between groups are controlled for. While recent studies suggest that researchers in various disciplines seldom comply with these requirements, lack of rigor in methodological reporting makes a proper, large-scale, and transdisciplinary assessment of the situation impossible. Given this situation, the present paper attempts a large-scale and transdisciplinary assessment of statistical reporting transparency in ANOVA-related papers, using customized regular expressions on the full-text of articles published in Elsevier journals. Results show that, beyond important variations at multiple disciplinary levels, reporting transparency in the testing of ANOVA statistical assumptions is generally lacking, as a minority of ANOVA-related papers refer to either homoscedasticity or multiplicity correction. Also, articles from higher-quartile journals tend to report statistical assumptions more thoroughly than those from lower-quartile journals, regardless of discipline or subdiscipline.
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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 | 0.496 | 0.858 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".