A comparison of the MMPI-2-RF and PAI overreporting indicators in a civil forensic sample with emphasis on the Response Bias Scale (RBS) and the Cognitive Bias Scale (CBS).
Bibliographic record
Abstract
The Cognitive Bias Scale (CBS; Gaasedelen, Whiteside, Altmaier, Welch, & Basso, 2019) was developed as a Personality Assessment Inventory (PAI) indicator of poor performance on Performance Validity Tests (PVTs) in a neuropsychological context. The current study aimed to investigate the effectiveness of the CBS in a forensic disability sample through a series of analyses by comparing it to other PAI validity scales and the Minnesota Multiphasic Personality Inventory (MMPI)-2-RF overreporting scales with an emphasis on the Response Bias Scale (RBS), which guided the development of the CBS. The participants in this study were drawn from an archival dataset containing 588 consecutive civil disability claimants. Findings showed the RBS and the CBS yielded similar patterns of negative correlations to PVTs, with RBS effect sizes being somewhat larger in most comparisons. Results of ANOVAs showed that the RBS produced the largest effect sizes in distinguishing between incentive only versus probable/definite malingered neurocognitive dysfunction (MND) groups, followed by the CBS. Estimates of sensitivity and specificity were comparable between the RBS and CBS at liberal cut scores, but the RBS was more specific to detecting Probable/Definite MND at more conservative cutoffs. Hierarchical logistic regression analyses showed that RBS accounted for 6% variance over CBS in the probable/definite MND classification, whereas the CBS accounted for 2% variance beyond the RBS. Overall, the results of this study support the utility of the CBS as the most effective PAI validity scale for detecting MND in a civil disability sample, and the RBS generally outperformed the CBS to some degree in all analyses. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".