Continuous Predictors of Pretest-Posttest Change: Highlighting the Impact of the Regression Artifact
Bibliographic record
Abstract
Researchers are often interested in exploring predictors of change, and commonly use a regression based model or a gain score analysis to compare degree of change across groups. Methodologists have cautioned against the use of the regression based model when there are non-random group differences at baseline because this model inappropriately corrects for baseline differences. Less research has addressed the issues that arise when exploring continuous predictors of change (e.g., a regression model with posttest as the outcome and pretest as a covariate). If continuous predictors of change correlate with pretest scores, the modeled relationship between predictors and change may be an artefact. This two-part study explored the statistical artefact that may arise when continuous predictors of change are included in pretest-posttest regression based models. Study 1 revealed that the problematic regression based model is currently being applied in psychology literature more often than non-problematic models, and that the conditions leading to the artefact were met in a significant amount of studies reviewed. Study 2 demonstrated that the artefact arises in conditions common within psychological research, and directly impacts Type I error rates. Recommendations are provided regarding which regression based models are appropriate when pretest scores are correlated with continuous predictors.
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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 | 0.240 | 0.572 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".