Self–Other Agreement in Personality Reports: A Meta-Analytic Comparison of Self- and Informant-Report Means
Bibliographic record
Abstract
Self-report questionnaires are the most commonly used personality assessment despite longstanding concerns that self-report responses may be distorted by self-protecting motives and response biases. In a large-scale meta-analysis ( N = 33,033; k = 152 samples), we compared the means of self- and informant reports of the same target's Big Five personality traits to examine the discrepancies in two rating sources and whether people see themselves more positively than they are seen by others. Inconsistent with a general self-enhancement effect, results showed that self-report means generally did not differ from informant-report means (average δ = -.038). Moderate mean differences were found only when we compared self-reports with stranger reports, suggesting that people are critical of unacquainted targets. We discuss implications of these findings for personality assessment and other fields in which self-enhancement motives are relevant.
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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 | 0.031 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".