State Resident Neuroticism Accounts for Life Satisfaction Differences Between Conservative and Liberal States of the USA
Bibliographic record
Abstract
Past research indicates associations between higher conservatism and higher life satisfaction, lower neuroticism and higher life satisfaction, and higher conservatism and lower neuroticism. Qualified deduction led to the following hypothesis: Neuroticism can account for the association between higher conservatism and higher life satisfaction. The 50 American states served as the units of analysis. Responses of 619,397 residents to the 44-item Big Five Inventory in an internet survey conducted from 1999 to 2005 provided mean neuroticism scores for each state. Conservative-liberal leaning of over 84,000 respondents to CBS News/New York Times polls from 1999 to 2003 and the percent voting Republican in each state in the 2000 to 2008 presidential elections combined to form a conservatism score for each state. The Gallup-Healthways Well-Being Index provided life satisfaction scores for over 1,000,000 respondents, transforming to a 2008 to 2010 composite score for each state. In a sequential multiple regression equation with life satisfaction as the criterion, state socioeconomic status and white population percent entered first as a block, conservatism entered second, and neuroticism entered third, the demographic controls accounted for 45.7% of the variance, conservatism accounted for another 10.4%, and neuroticism accounted for an additional 10.6%. However, with the entry order of conservatism and neuroticism reversed, neuroticism accounted for another 19.6% but conservatism accounted for only an additional nonsignificant 1.4%. Therefore, the hypothesis was supported. Three alternative explanations suggested by other researchers were not supported in the state-level analysis.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".