Clarifying problems and offering solutions for correlated error when assessing the validity of selected-subtest short forms.
Bibliographic record
Abstract
The correlation between a short-form (SF) test and its full-scale (FS) counterpart is a mainstay in the evaluation of SF validity. However, in correcting for overlapping error variance in this measure, investigators have overattenuated the validity coefficient through an intuitive misapplication of P. Levy's (1967) formula. The authors of the present article clarify that such corrections should be based on subtest-level versus FS-level data. Additionally, the authors propose a simple, modified equation incorporating FS-level scores that provides liberal and conservative validity measures for comparison across estimation methods, and they demonstrate its use in both a normative (N = 2,450) and clinical psychiatric (N = 216) sample.
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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 | 0.229 | 0.567 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".