Comparing the Ability of MMPI-2 and MMPI-2-RF Validity Scales to Detect Feigning: A Meta-Analysis
Bibliographic record
Abstract
Several meta-analyses of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) and Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) have examined these instruments' ability to detect symptom exaggeration or feigning. However, limited research has directly compared whether the scales across these two instruments are equally effective. This study used a moderated meta-analysis to compare 109 MMPI-2 and 41 MMPI-2-RF feigning studies, 83 (56.46%) of which were not included in previous meta-analyses. Although there were differences between the two test versions, with most MMPI-2 validity scales generating larger effect sizes than the corresponding MMPI-2-RF scales, these differences were not significant after controlling for study design and type of symptoms being feigned. Additional analyses showed that the F and Fp-r scales generated the largest effect sizes in identifying feigned psychiatric symptoms, while the FBS and RBS were better at detecting exaggerated medical symptoms. The findings indicate that the MMPI-2 validity scales and their MMPI-2-RF counterparts were similarly effective in differentiating genuine responders from those exaggerating or feigning psychiatric and medical symptoms. These results provide reassurance for the use of both the MMPI-2 and MMPI-2-RF in settings where symptom exaggeration or feigning is likely. Findings are discussed in the context of the recently released MMPI-3.
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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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.055 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".