Pairwise Multiple Comparison Test Procedures: An Update for Clinical Child and Adolescent Psychologists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Locating pairwise differences among treatment groups is a common practice of applied researchers. Articles published in this journal have addressed the issue of statistical inference within the context of an analysis of variance (ANOVA) framework, describing procedures for comparing means, among other issues. In particular, 1 article (Jaccard & Guilamo-Ramos, 2002b) presented some new methods of performing contrasts of means whereas another presented a framework for obtaining robust tests within this same context (Jaccard & Guilamo-Ramos, 2002a). The purpose of this article is to add to these contributions by presenting some newer methods for conducting pairwise comparisons of means, that is by extending the contributions of the first article and applying the framework of the second article to pairwise multiple comparisons. The newer methods are intended to provide additional sensitivity to detect treatment group differences and provide tests that are robust to the effects of variance heterogeneity, nonnormality, or both.
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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 | 0.030 | 0.269 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.004 | 0.010 |
| 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 it