Extraverted, but Lonely: Noise- and Bias-free ‘True’ Correlations Between Loneliness and the Big Five Personality Domains
Bibliographic record
Abstract
We conducted the largest study to date to examine the true associations between loneliness and personality traits combining self and informant reports. Using a method described by Mõttus and colleagues (2023) we obtained noise- and bias-free ‘true’ correlations (rtrues), which were used to produce regression model estimates of the true independent associations (βtrues) of age, sex and Big Five personality domains with loneliness across three multicultural samples (Estonian-speaking, n = 20,893; Russian-speaking, n = 762; English-speaking, n = 600). Using this method, personality traits predicted 54–62% of variance in loneliness, compared to 26–33% when using methods limited to just self-report or informant-report data. Across samples, we found strong independent relationships between loneliness and Neuroticism (βtrues around 0.7) when controlling for other personality traits. Loneliness had robust, though comparatively weaker, associations with Extraversion (βtrues around 0.25) and Agreeableness (βtrues from 0.1 to 0.3). Women and older people tended to be lonelier, but these associations were entirely attributable to personality trait differences: once these were controlled for, the associations either reversed or became non-significant. Overall, we find that loneliness is far more closely related to Neuroticism than previously understood, and distinct from both social isolation and social disconnection.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".