Improving the Meta-Analytic Assessment of Effect Size Variance With an Informed Bayesian Prior
Bibliographic record
Abstract
Meta-analytic estimation of effect size variance is critical for determining the degree to which a relationship or finding generalizes across contexts. In most meta-analyses, population effect size variability is estimated by subtracting expected sampling error variance from observed variance, using only information from a limited set of available studies. We propose an improved Bayesian variance estimation technique that incorporates findings from previous meta-analytic research through an informed prior distribution of likely levels of effect size variance. The logic of exchangeability as a conceptual foundation for using an informed prior is explicated. On the basis of Monte Carlo simulations, we find the traditional method of meta-analytic variance estimation the most biased and least accurate technique across all sizes of meta-analyses considered. The Bayesian methodology incorporating an informed prior proved to be the most accurate and overall least biased of all estimation methods. Conceptual advantages and limitations that must be taken into account when incorporating an informed prior to estimate variability of effect sizes in a meta-analysis are also discussed.
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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 | 0.305 | 0.625 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| 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".