Do MMPI-3 Validity Scale Findings Generalize to Concurrently Administered Measures? Validation with a Forensic Disability Sample
Bibliographic record
Abstract
OBJECTIVE: Research has demonstrated that over-reporting and under-reporting, when detected by the MMPI-2/-RF Validity Scales, generalize to responses to other self-report measures. The purpose of this study was to investigate whether the same is true for the Minnesota Multiphasic Personality Inventory-3 (MMPI-3) Validity Scales. We examined the generalizability of over-reporting and under-reporting detected by MMPI-3 Validity Scales to extra-test self-report, performance-based, and performance validity measures. METHOD: The sample included 665 majority White, male disability claimants who, in addition to the MMPI-3, were administered several self-report measures, some with embedded symptom validity tests (SVTs), performance-based measures, and performance validity tests (PVTs). Three groups were identified based on MMPI-3 Validity Scale scores as over-reporting (n = 276), under-reporting (n = 100), or scoring within normal limits (WNL; n = 289). RESULTS: Over-reporting on the MMPI-3 generalized to symptom over-reporting on concurrently administered self-report measures of psychopathology and was associated with evidence of over-reporting from other embedded SVTs. It was also associated with poorer performance on concurrently administered measures of cognitive functioning and PVTs. Under-reporting on the MMPI-3 generalized to symptom minimization on collateral measures of psychopathology. On measures of cognitive functioning, we found no differences between the under-reporting and WNL groups, except for the Wisconsin Card Sorting Test-64 Card Version and Wide Range Achievement Test-Fifth Edition (each with negligible effect sizes). CONCLUSIONS: MMPI-3 Validity Scales can identify possible over- and under-reporting on concurrently administered measures. This can be of particular value when such measures lack validity indicators.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".