Toward a Resolution of the Tripartite Structure of Subjective Well‐Being
Bibliographic record
Abstract
Diener (1984) introduced the concept of "subjective well-being" (SWB) as comprising three primary components: life satisfaction (LS), positive affect (PA), and negative affect (NA). Busseri and Sadava (2011) identified multiple competing conceptualizations of the tripartite structure of SWB and delineated problems with this ambiguity with respect to defining, operationalizing, analyzing, and synthesizing information concerning SWB. The present work provides an empirical evaluation of four competing structural approaches in which SWB is conceptualized variously as three separate components (Model 1), a hierarchical construct (Model 2), a causal system (Model 3), and a composite (Model 4). Data from a longitudinal study of middle-aged Americans (N = 3,707; 20-75 years old, 55% female, 94% Caucasian) were used to examine the relatedness versus independence of the three SWB components within and across time, as well as predictive effects on SWB. The various structural models differ in how adequately they accommodate the joint relatedness/independence of the SWB components and lead to different conclusions concerning predictive effects on SWB. Conceptual and empirical considerations are considered within and across models. Implications and next steps for further understanding the tripartite structure of SWB 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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".