Separating method factors and higher order traits of the Big Five: A meta-analytic multitrait–multimethod approach.
Bibliographic record
Abstract
Though most personality researchers now recognize that ratings of the Big Five are not orthogonal, the field has been divided about whether these trait intercorrelations are substantive (i.e., driven by higher order factors) or artifactual (i.e., driven by correlated measurement error). We used a meta-analytic multitrait-multirater study to estimate trait correlations after common method variance was controlled. Our results indicated that common method variance substantially inflates trait correlations, and, once controlled, correlations among the Big Five became relatively modest. We then evaluated whether two different theories of higher order factors could account for the pattern of Big Five trait correlations. Our results did not support Rushton and colleagues' (Rushton & Irwing, 2008; Rushton et al., 2009) proposed general factor of personality, but Digman's (1997) α and β metatraits (relabeled by DeYoung, Peterson, and Higgins (2002) as Stability and Plasticity, respectively) produced viable fit. However, our models showed considerable overlap between Stability and Emotional Stability and between Plasticity and Extraversion, raising the question of whether these metatraits are redundant with their dominant Big Five traits. This pattern of findings was robust when we included only studies whose observers were intimately acquainted with targets. Our results underscore the importance of using a multirater approach to studying personality and the need to separate the causes and outcomes of higher order metatraits from those of the Big Five. We discussed the implications of these findings for the array of research fields in which personality is studied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.141 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.026 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".