Determining the Significance of Correlations Corrected for Unreliability and Range Restriction
Why this work is in the frame
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Bibliographic record
Abstract
A new asymptotic formula for estimating the sampling variance of a correlation coefficient corrected for unreliability and range restriction was proposed. A Monte Carlo assessment of the new sampling variance formula has resulted in the following conclusions. First, the formula-based (analytical) sampling variances were very close to the empirically derived sampling variances based on 5,000 replications. Second, the sampling variance formula was quite robust against committing Type I errors. Third, the statistical power was low to moderate in distinguishing between two unattenuated and unrestricted population correlations. Fourth, the new formula produced smaller sampling variances; was closer to nominal alpha levels; and was more powerful when sample size increased, when the population correlation coefficient increased, when range restriction was less severe, and when both the criterion and predictor reliabilities increased.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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