Happy Days: Resolving the Structure of Daily Subjective Well-Being, Between and Within Individuals
Bibliographic record
Abstract
We address the long-standing confusion concerning the conceptualization and structure of subjective well-being (SWB) by examining daily variation in life satisfaction (LS), positive affect (PA), and negative affect (NA). A total of 911 participants provided daily ratings of LS, PA, and NA over 14 days. Between- and within-individual variations in daily SWB were simultaneously modeled using dynamic structural equation modeling and random intercept cross-lagged panel modeling. Parameter estimates were highly consistent across approaches. Strong loadings from LS, PA, and NA were observed on latent SWB factors, both between and within individuals; cross-lagged predictive effects among SWB components were small and inconsistent across adjacent days within individuals. Findings provide compelling new evidence supporting a hierarchical conceptualization of SWB as an underlying (latent) sense of well-being reflected in daily experiences of LS, PA, and NA. Implications for studying stable (trait-like) and dynamic (time-varying) aspects of other multidimensional constructs in social and personality psychology are discussed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".