Investigating Relative and Absolute Methods of Measuring HEXACO Personality Using Self- and Observer Reports
Bibliographic record
Abstract
Abstract. Based on the principles of social comparison theory, the relative percentile (RP) method is an alternative approach to the measurement of psychological characteristics. It involves asking raters to explicitly estimate the percentage of a comparison group that they believe is lower than the target on a characteristic. This study explored the RP method for the measurement of personality. Specifically, we investigated the convergence of the RP with traditional (i.e., Likert-type) personality measures and the convergence between self- and observer reports. Both members of 142 Australian well-acquainted dyads rated themselves and their counterpart using the traditional Likert-type HEXACO-100 and a 25-item RP assessment of the HEXACO facets. Two weeks later, 78 participants completed the RP assessment again, allowing the assessment of test-retest reliability. The RP ratings showed mostly moderate reliability, though generally lower reliability than their corresponding traditional scales, and a relatively clear HEXACO factor structure. Furthermore, the RP ratings correlated significantly with the Likert-type ratings from the same rater (e.g., self–self) and with RP ratings from a different rater (i.e., self–observer), although convergence did vary by HEXACO domain. One potential issue with RP ratings, however, is that they mostly yielded Gaussian distributions, instead of the theoretically expected uniform distribution, which may suggest that it is challenging for respondents to estimate percentiles.
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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 | 0.058 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".