Identification of Real and Artifactual Moderators of Effect Size in Meta-Analysis
Bibliographic record
Abstract
This article argues that while meta-analytic studies are widely used in psychological literature, heterogeneity and the potential for confounding remain major problems in the interpretation of meta-analytic study results. The article demonstrates the use of exploratory analysis including graphical methods prior to meta-analysis, and introduces a methodology to screen for artifactual effects. These procedures are illustrated on effect size data comparing depression treatment outcome from psychotherapy versus pharmacotherapy. Results support prior findings of a nonsignificant difference in effect size between the two treatments. They also support findings that treatment type accounts for only a very small proportion of outcome variance. However, the results indicate that some previously reported covariates of depression treatment outcome may be artifactual.
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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 | 0.473 | 0.636 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.031 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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 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".