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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".