The impact of underreporting and overreporting on the validity of the Personality Inventory for DSM–5 (PID-5): A simulation analog design investigation.
Bibliographic record
Abstract
. Despite its relatively recent introduction, the PID-5 has generated an impressive accumulation of studies examining its psychometric properties, and the instrument is also already widely and frequently used in research studies. Although the PID-5 is psychometrically sound overall, reviews of this instrument express concern that this scale does not possess validity scales to detect invalidating levels of response bias, such as underreporting and overreporting. McGee Ng et al. (2016), using a "known-groups" (partial) criterion design, demonstrated that both underreporting and overreporting grossly affect mean scores on PID-5 scales. In the current investigation, we replicate these findings using an analog simulation design. An important extension to this replication study was the finding that the construct validity of the PID-5 was also significantly compromised by response bias, with statistically significant attenuation noted in validity coefficients of the PID-5 domain scales with scales from other instruments measuring congruent constructs. This attenuation was found for underreporting and overreporting bias. We believe there is a need to develop validity scales to screen for data-distorting response bias in research contexts and in clinical assessments where response bias is likely or otherwise suspected. (PsycINFO Database Record
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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 | 0.061 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".