Measurement Reliability, the Spearman–Jensen Effect and the Revised Thorndike Model of Test Bias
Bibliographic record
Abstract
The Thorndike model of test fairness has recently been revised and used to argue that cognitive ability tests are biased against certain groups of test‐takers because ability tests show larger mean differences across racial groups than do job performance measures. We discuss two critical factors that confound this new version of Thorndike's model, making it susceptible to false indications of test bias. Those factors are (a) measurement error (i.e., reliability) in both the predictor and criterion and (b) the Spearman–Jensen effect (i.e., the well‐documented effect that group differences in observed g‐saturated measures are directly proportional to the degree the manifest indicator reflects g). Finally, because the Spearman–Jensen effect is not well known within the applied literature, we present a brief simulation to better elucidate the implications of the Spearman–Jensen effect for personnel selection in general, and claims of bias in cognitive ability testing in particular.
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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 | 0.086 | 0.342 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".