Revisiting Values and Self-Esteem: A Large-Scale Study in the United States
Bibliographic record
Abstract
Person-culture fit perspectives posit that individuals have higher self-esteem when their values match the values of the sociocultural environment in which they live. The current study tested this hypothesis by examining the associations between value congruence and self-esteem in a large-scale sample in the United States ( N = 48,563). Multilevel response surface analyses revealed no evidence of value congruence effects on self-esteem, such that the agreement between individual- and state-level values did not positively predict self-esteem for any of the 10 basic values. Instead, we found positive (stimulation, security) and negative (conformity) linear associations between individual-level values and self-esteem. We also found positive curvilinear relationships between individual-level achievement and tradition values and self-esteem, and negative curvilinear relationships between individual-level self-direction, hedonism, power, benevolence, and universalism values and self-esteem. In addition, state-level values moderated the relationship between values and self-esteem for tradition, universalism, and conformity values. In federal states with stronger endorsement of tradition values, individuals’ tradition values were more positively associated with self-esteem. In contrast, in states with stronger endorsement of universalism values, individuals’ universalism values were more negatively associated with self-esteem. Lastly, individuals’ conformity values were negatively associated with self-esteem, particularly in states with weaker endorsement of conformity values.
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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".