Aggregating validity indicators: The salience of domain specificity and the indeterminate range in multivariate models of performance validity assessment
Bibliographic record
Abstract
This study was designed to examine the “domain specificity” hypothesis in performance validity tests (PVTs) and the epistemological status of an “indeterminate range” when evaluating the credibility of a neuropsychological profile using a multivariate model of performance validity assessment. While previous research suggests that aggregating PVTs produces superior classification accuracy compared to individual instruments, the effect of the congruence between the criterion and predictor variable on signal detection and the issue of classifying borderline cases remain understudied. Data from a mixed clinical sample of 234 adults referred for cognitive evaluation (MAge = 46.6; MEducation = 13.5) were collected. Two validity composites were created: one based on five verbal PVTs (EI-5VER) and one based on five nonverbal PVTs (EI-5NV) and compared against several other PVTs. Overall, language-based tests of cognitive ability were more sensitive to elevations on the EI-5VER compared to visual-perceptual tests; whereas, the opposite was observed with the EI-5NV. However, the match between predictor and criterion variable had a more complex relationship with classification accuracy, suggesting the confluence of multiple factors (sensory modality, cognitive domain, testing paradigm). An “indeterminate range” of performance validity emerged that was distinctly different from both the Pass and the Fail group. Trichotomized criterion PVTs (Pass-Borderline-Fail) had a negative linear relationship with performance on tests of cognitive ability, providing further support for an “in-between” category separating the unequivocal Pass and unequivocal Fail classification range. The choice of criterion variable can influence classification accuracy in PVT research. Establishing a Borderline range between Pass and Fail more accurately reflected the distribution of scores on multiple PVTs. The traditional binary classification system imposes an artificial dichotomy on PVTs that was not fully supported by the data. Accepting “indeterminate” as a legitimate third outcome of performance validity assessment has the potential to improve the clinical utility of PVTs and defuse debates regarding “near-Passes” and “soft Fails.”
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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 | 0.118 | 0.295 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".