Response bias and the Personality Inventory for DSM–5: Contrasting self- and informant-report.
Bibliographic record
Abstract
Previous research has raised concerns that scores derived from the Personality Inventory for DSM-5 (PID-5; American Psychiatric Association, 2013; Krueger, Derringer, Markon, Watson, & Skodol, 2012) may be compromised by response styles such as underreporting or overreporting. The informant-report form of the PID-5 (PID-5-IRF; Markon, Quilty, Bagby, & Krueger, 2013) has been recommended for use when response bias is an assessment concern. The purpose of the current investigation was to evaluate PID-5 and PID-5-IRF scale score elevations across participants exhibiting signs of overreporting or underreporting. A total of 245 adults completed the PID-5 and the Revised NEO Personality Inventory (NEO PI-R; Costa & McCrae, 1992). A family member or friend of at least 1 year's acquaintance completed the PID-5-IRF for 216 of these. A total of 211 target-informant pairs were available for analysis. Participants were categorized as overreporting and underreporting according to NEO PI-R validity scale cutoffs. The majority of PID-5 scale scores were elevated in those identified as overreporting; more than half of the PID-5-IRF scale scores were similarly elevated. The majority of PID-5 scale scores were lower in those scoring above underreporting cut-offs; however, PID-5-IRF scales were not as consistently or strongly impacted. PID-5 scales were strongly impacted by response bias, whereas PID-5-IRF scores were less strongly impacted overall, and more so by overreporting bias. Caution when using these instruments in the assessment of personality disorders prone to over- or underreporting may be warranted. (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 | 0.063 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".