Response Style and the Revised NEO Personality Inventory: Validity Scales and Spousal Ratings in a Chinese Psychiatric Sample
Bibliographic record
Abstract
The effects of response style bias on profile scores from the family of NEO scales and the resultant influence of response style on the predictive capacity of these scales continues to be debated. In this study, a large sample of Chinese psychiatric patients were categorized into four response style groups based on their scores from recently developed "validity" scales for the revised NEO Personality Inventory (NEO PI-R). Mean differences and correlations between self-report and spousal ratings of these patients were examined for the NEO PI-R domain and facet scales. Excessive positive self-presentation bias resulted in mean differences between the self-report and spousal ratings for N and E. Correlations between self-report and spousal ratings were reduced in patients engaging in positive self-presentational bias compared to those who were not so categorized on three of the five NEO PI-R scales. However, these results were manifest only in a sub-sample of psychotic patients. Negative self-presentational bias did not affect mean differences or diminish the correlations between the self-report and spousal ratings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".