Bootstrap confidence intervals for the mean correlation corrected for Case IV range restriction: A more adequate procedure for meta-analysis.
Bibliographic record
Abstract
In this study, we proposed to use the nonparametric bootstrap procedure to construct the confidence interval for the mean correlation r corrected for Case IV range restriction in meta-analysis (i.e., ; Hunter, Schmidt, & Le, 2006). A comprehensive Monte Carlo study was conducted to evaluate the accuracy of the parametric confidence interval and 3 nonparametric bootstrap confidence intervals for r(c4). Of the 4 intervals, our results showed that the bootstrap bias-corrected and accelerated percentile interval (BCaI) yielded the most accurate results across different data situations. In addition, the mean-corrected correlation r(c4) was found to be more accurate than the uncorrected estimate. Implications of the mean-corrected correlation r(c4) and BCaI in organizational studies are also discussed.
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How this classification was reachedexpand
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 | 0.070 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, not a consensus.
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".