Development and Validation of the Malingering Discriminant Function Index for the MMPI–2
Bibliographic record
Abstract
The predictive capacity of the MMPI-2 (Butcher et al., 2001) "fake-bad" validity scales (F, FB, and FP) is diminished when respondents have knowledge (i.e., coached) about the operating characteristics of these scales. In this investigation, we endeavored to develop a MMPI-2 fake bad validity index that would be less vulnerable to validity-scale knowledge. Applying discriminant function procedures, we derived a set of weighted Clinical and Content scales that reliably distinguished large samples of validity-scale coached undergraduate research participants instructed to feign mental illness (n = 534) from psychiatric patient samples (n = 590). We subsequently validated this Malingering Discriminant Function Index (M-DFI) in independent samples of research participants (n = 230) and patients (n = 300) and showed relatively less attenuation in predictive capacity compared with F, FB, and FP across uncoached and validity scale coached feigning conditions.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".