Familial Transmission of Personality Traits and Life Satisfaction Is Higher Than Shown in Typical Single-Method Studies
Bibliographic record
Abstract
Personality trait similarity among ordinary relatives is surprisingly low, with parent-offspring and sibling-sibling correlations usually being r ≤ .15. We explain why these correlations are distorted in typical single-method studies and argue that this issue can only be addressed with multi-method designs. We also explain why non-twin relative comparisons can provide a more generalizable way of estimating (additive, narrow-sense) heritability than the better-known twin comparisons. In a sample of parent-offspring (Npairs = 522), sibling-sibling (Npairs = 388), and second-degree relative pairs (Npairs = 476), who rated their Big Five personality traits and life satisfaction and were each rated by an independent informant (Nparticipants = 2,258 + informants), we found that parent-offspring and sibling trait correlations were about a third higher than typically shown (r ≈ .20). Based on the ordinary relative comparisons, the heritability of personality traits and life satisfaction was around 40%, up from about 26% typical in self-report studies. Life satisfaction was as heritable as personality traits, with about 80% of the genetic variance shared with neuroticism, extraversion, and conscientiousness. About half of life satisfaction’s phenotypic correlations with neuroticism and extraversion and all its correlation with conscientiousness were accounted for by shared genetic factors. Using data from a larger relatives sample with only self-reports (Nparticipants = 32,004; Npairs = 24,118), we provide further evidence that growing up together does not make people more similar. Additionally, the findings were consistent for both aggregate traits and individual items. Only multi-method designs can reveal traits’ true similarity among relatives, and genetic and environmental transmission.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".