A critical re‐examination and analysis of cognitive ability tests using the Thorndike model of fairness
Bibliographic record
Abstract
The literature investigating the bias of cognitive ability tests (CATs) is often conflated with the controversy surrounding which method for determining test bias is superior. The general acceptance of the Cleary (1968) model of test bias in industrial/organizational psychology has served to deter evaluations of tests against other models of test bias because acceptance of the Cleary model as ‘superior’ implies the limited relevance of investigations of tests against other models of bias. Although these other models are not considered to be models of predictive bias in the psychometric sense, they nonetheless have significant implications for workplace diversity. Most notably, the existing literature lacks the precision and depth necessary to extrapolate the actual false‐rejection rate in selection decisions that burden visible minority groups when CATs are used. The current study identifies these gaps in the literature in addition to evaluating CATs against the Thorndike (1971) model of test bias. Results indicate that a one standard deviation (SD) difference in Black‐White CAT scores is associated with a Black‐White difference in job performance of approximately 1/3 SD. The Black‐White difference in job performance is reduced to approximately 1/10 SD when objective, rather than subjective, job performance criteria are used. We therefore conclude that CATs are biased against Blacks when evaluated using the Thorndike model. The implications for use of CATs in personnel selection are discussed.
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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 | 0.069 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".