Cross-Cultural Comparison of Personality Traits, Attachment Security, and Satisfaction With Relationships as Predictors of Subjective Well-Being in India, Sweden, and the United States
Bibliographic record
Abstract
Personality traits like Neuroticism and Extroversion, Satisfaction With Relationships, and Attachment Security are among the most important predictors of subjective well-being (SWB). However, the relative contribution of these predictors to SWB is seldom tested, and even more rarely tested cross-culturally. In this study, we replicate and extend Galinha, Oishi, Pereira, Wirtz, and Esteves, aiming to identify the strongest predictors of SWB, and in what way that contribution is universal or culture-specific, across such collectivist-individualist countries as India, Sweden, and the United States ( N = 1,622). Structural equation modeling showed that Satisfaction With Relationships is a stronger predictor of SWB in India, while Neuroticism is a stronger predictor of SWB in Sweden and the United States, results consistent with prior Portuguese and Mozambican samples. These findings suggest that Satisfaction With Relationships is probably a stronger predictor of SWB in more collectivistic and less developed countries, while low Neuroticism is a stronger predictor of SWB in more individualistic and highly developed countries. Across all samples, Attachment Security and Extroversion showed very weak or nonsignificant effects on SWB above the contribution of Neuroticism and Satisfaction With Relationships, consistent with prior results. Neuroticism significantly mediated the relationship between Attachment Security, SWB, and Satisfaction With Relationships.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".