The Predictive Capacity of the MMPI-2 and PAI Validity Scales and Indexes to Detect Coached and Uncoached Feigning
Bibliographic record
Abstract
The objective of this study was to examine the relative effectiveness of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) and the Personality Assessment Inventory (PAI) validity scales and indexes to detect malingering. Research participants were either informed (coached) or not informed (uncoached) about the presence and operating characteristics of the validity scales and instructed to fake bad on both the MMPI-2 and PAI. The validity scale and index scores produced by these research participants were then compared to those scores from a bona fide sample of psychiatric patients (n = 75). Coaching had no effect on the ability of the research participants to feign more successfully than those participants who received no coaching. For the MMPI-2, the Psychopathology F scale, or F(p), proved to be the best at distinguishing psychiatric patients from research participants instructed to malinger, although the other F scales (i.e., F and Fb) were also effective. For the PAI, the Rogers Discriminant Function index (RDF) was clearly superior to the other PAI fake-bad validity indicators; neither the Negative Impression Management scale nor Malingering Index were effective at detecting malingered profiles in this study. Overall, RDF proved to be marginally superior to F and F(p) in distinguishing MMPI-2 and PAI protocols produced by research participants asked to malinger and psychiatric patients. Both the RDF and the F and F(p) scales, however, were able to increase the predictive capability of one another.
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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 | 0.008 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".