The Other Side of the Bell Curve: Multivariate Base Rates of High Scores on the Delis-Kaplan Executive Function System
Bibliographic record
Abstract
OBJECTIVE: Previous researchers have examined the frequency at which healthy participants obtain one or more low scores on neuropsychological test batteries, proposing five psychometric principles of multivariate base rates: (a) low scores are common, with their frequency contingent on (b) the low score cutoff used, (c) the number of tests administered/interpreted, and (d) the demographic characteristics and (e) intelligence of participants. The current study explored whether these principles applied to high scores as well, using the Delis-Kaplan Executive Function System (D-KEFS). METHOD: Multivariate base rates of high scores (≥75th, ≥84th, ≥91st, ≥95th, and ≥98th percentiles) were derived for a three-test, four-test, and full D-KEFS battery, using the adult portion of the normative sample (aged 16-89 years; N = 1050) stratified by education and intelligence. The full D-KEFS battery provides 16 total achievement scores (primary indicators of executive function). RESULTS: High scores occurred commonly for all batteries. For the three-test battery, 24.1% and 12.4% had 1 or more scores ≥95th percentile and ≥98th percentile, respectively. High scores occurred more often for longer batteries: 61.6%, 72.9%, and 87.8% obtained 1 or more scores ≥84th percentile for the three-test, four-test, and full batteries, respectively. The frequency of high scores increased with more education and higher intelligence. CONCLUSIONS: The principles of multivariate base rates also applied to high D-KEFS scores: high scores were common and contingent on the cutoff used, number of tests administered/interpreted, and education/intelligence of examinees. Base rates of high scores may help clinicians identify true cognitive strengths and detect cognitive deficits in high functioning people.
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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 | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".