Measurement invariance of the Personality Inventory for DSM-5 across sex
Bibliographic record
Abstract
Introduction: There has been an international movement towards dimensional models of personality disorders (PDs) in the last decades, which culminated in the publication of the Alternative Model of Personality Disorders (AMPD) in the Emerging Measures and Models section of the DSM-5. This model was accompanied by a APA-sanctioned Personality Inventory for DSM-5 (PID-5) for the assessment of the AMPD pathological personality traits. One major issue with the assessment of personality disorders pertains to sex differences, and measurement invariance across sex in assessment instruments for PDs is necessary in order to ensure non-biased evaluations and to make valid comparisons between men and women. This study aimed to provide more information on measurement invariance across sex for the PID-5, using both the original scoring approach provided by the authors of the instrument and the scoring approach suggested by the APA in the published version of the PID-5. Methods: This study was conducted with a sample of 2273 participants from the general Québec (Canada) adult population aged 18 to 90 years (M = 46.59; SD = 16.32; 51.8% women). Results: The original scoring approach model showed good fit to data after freeing paths between certain traits and reached strict invariance. The APA scoring approach also showed good fit to data and reached strict invariance, but needed an adjustment (path freed between Emotional lability and Impulsivity in men) to reach scalar invariance. Discussion: In line with previous research, the PID-5 is invariant across sex and the five-factor structure adjusts well to data. The APA scoring approach appears to attenuate the cross-loading problem observed with the original scoring approach. In light of these results, we recommend using the APA scoring approach to derive domain scores.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".