The happy personality revisited: Re‐examining associations between Big Five personality traits and subjective well‐being using meta‐analytic structural equation modeling
Bibliographic record
Abstract
OBJECTIVE/BACKGROUND: Using meta-analytic structural equation modeling (MASEM), we examined the link between Big Five personality traits and subjective well-being (SWB), operationalized as three separate components and as a latent factor indicated by life satisfaction (LS), positive affect (PA), and negative affect (NA). PA and NA were assessed based on frequency of a broad range of affective experiences, rather than intensity of high arousal affective experiences, thus excluding studies using the Positive and Negative Affect Schedule. METHOD: 35 samples were included, encompassing 22,135 participants from 14 countries, in which all eight variables were assessed. RESULTS: Correlations among personality traits were moderate, on average, and the latent SWB factor had strong loadings from all three components. Personality traits together explained substantial variance in LS, PA, and NA, and in the latent SWB factor, with unique predictive effects on the latent factor from each personality trait except openness. Associations between personality traits and SWB components were fully accounted for by a latent SWB factor, with one exception: A specific association was found between neuroticism and unique variance in NA. CONCLUSIONS: The present findings provide new insights concerning the notion of a 'happy personality' in showing that Big Five personality traits have unique associations with an underlying sense of SWB.
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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 | 0.046 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.021 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".