Contextualizing Statistical Suppression Within Pretest-Posttest Designs
Why this work is in the frame
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Bibliographic record
Abstract
Statistical suppression occurs when adjusting for some third variable enhances or substantially modifies the association between an initial predictor and an outcome. Although many methodologists have discussed this phenomenon, very little work has examined suppression in longitudinal regression models such as the pretest-posttest design. This research addressed this gap with two separate studies. Study One was a literature review that reviewed 80 articles from a variety of fields within psychology. Study Two was an analysis of a large longitudinal clinical dataset via 925 statistical models. Both studies revealed consistent results: in approximately 20% of instances, suppression effects were observed and were attributable to the inclusion of a pretest measure. Results underscore that controlling for pretest measures when assessing change may be of value, as this can help clarify the associations between predictors and posttest outcomes.
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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 | 0.004 | 0.005 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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