Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders
Bibliographic record
Abstract
IQ scores are volatile indices of global functional outcome, the final common path of an individual's genes, biology, cognition, education, and experiences. In studying neurocognitive outcomes in children with neurodevelopmental disorders, it is commonly assumed that IQ can and should be partialed out of statistical relations or used as a covariate for specific measures of cognitive outcome. We propose that it is misguided and generally unjustified to attempt to control for IQ differences by matching procedures or, more commonly, by using IQ scores as covariates. We offer logical, statistical, and methodological arguments, with examples from three neurodevelopmental disorders (spina bifida meningomyelocele, learning disabilities, and attention deficit hyperactivity disorder) that: (1) a historical reification of general intelligence, g, as a causal construct that measures aptitude and potential rather than achievement and performance has fostered the idea that IQ has special status and that in studying neurocognitive function in neurodevelopmental disorders; (2) IQ does not meet the requirements for a covariate; and (3) using IQ as a matching variable or covariate has produced overcorrected, anomalous, and counterintuitive findings about neurocognitive function.
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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 | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".