Accuracy of Regression Equation Prediction Across the Range of Estimated Premorbid IQ
Bibliographic record
Abstract
Linear regression is often used to predict psychological criterion variables such as premorbid IQ. The prevailing method of evaluating the accuracy of prediction indicates poor accuracy for both high and low criterion values. These results have led to the conclusion that the equations are not applicable for predicting scores beyond about one standard deviation from the mean. The apparent inaccuracy at the extremes, however, is an artifact of inappropriate analysis. An alternative analysis method is described and used to re-analyze two sets of data. Empirical results show that both high and low WAIS-R IQ scores, predicted using versions of the NART, agree with actual measured IQ scores as accurately as do predicted scores near the mean. In addition, confidence intervals were only 5% larger for more extreme predicted values than for values near the mean, which would be of minor clinical consequence. The practical constraint on prediction of extreme values arises not from the regression technique, but from the limited range of the predictor variable(s). The two reviewed IQ prediction equations would, however, have adequate range for a high percentage of individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".