Reaching for the Brass Ring of Psychometric Test Standards: Commenting on Slaney, Storey, and Barnes
Bibliographic record
Abstract
Slaney, Storey, and Barnes ( 2011 ), in delineating guidelines for data-driven psychometric test evaluation, advocate modern test theory for extending a prescriptive framework proposed in Slaney and Maraun ( 2008 ). Here, with an emphasis on noncognitive tests, we begin with a historical review of psychological testing and the developments of testing theory up to current standards and practices. We argue that although the efforts of Slaney et al. are commendable and may be superior in a field in which classical test theory still dominates, they may fall upon deaf ears for two reasons. First, present psychology training is negligent in keeping up with recent developments in statistics and measurement. Second, the practical advantages of modern test theory over classical approaches have not yet been sufficiently demonstrated to test users. This is not to say modern theory cannot produce better tests, rather, when we consider that the ultimate criterion has been and will be predictive validity, old, unsophisticated, classically developed tests still seem to perform satisfactorily. The prescriptive guidelines put forth in Slaney et al. are therefore better understood as aspirational targets, as recommendations, as challenges for tomorrow's generations of test users and developers, and less like psychometric law.
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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 | 0.073 | 0.260 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.050 | 0.118 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".