Overestimated relationships with subjective well-being.
Bibliographic record
Abstract
This article is about relationships between subjective well-being (SWB) and variables such as demographics, intentional activities, personality traits, and personal characteristics. Causal interpretation of these relationships is usually asymmetric from the variable to SWB, although the literature also contains interpretations of reverse or bidirectional causality. Evidence reviewed here suggests that heritable personality traits may underlie some of these relationships. A consequence is that covariance may be lower than lower than indicated by phenotypic (within individual) correlations. The article discusses some implications for positive psychology. Keywords: subjective well-being, happiness, satisfaction, heritability, general factor of personality This article discusses models of probabilistic causation that encompass well-being measures and their correlates. Causal models contain a set of variables overlaid by two mathematical structures: a graph indicating causal direction in relationships between variables, and probabilistic estimates related to such relationships. Received opinion influences the former while methodological considerations overall confidence in a model (e.g., type of data, statistical power). Lyubomirsky, King, and Diener (2005) comment on an established bias in causal attribution even with data from one-time assessment: associations between desirable outcomes and happiness have led most investigators to assume that success makes people happy. (p. 803). A focus of the article is on comparison this assumption with alternative propositions. The article begins with a brief discussion of definitions and well-being measures used in positive psychology, followed by a description of causal assumptions that may pertain to relationships involving these measures. The next two sections respectively discuss correlates of well-being and the relevance of higher-order dispositions to these relationships. The final section considers some implications for positive psychology. Throughout the article, we highlight contributions by Canadian researchers. Definitions and Measures Definitions of well-being include the following. Subjective wellbeing (SWB) is an evaluation of in terms of satisfaction and the balance between positive and negative affect (Keyes, Shmotkin, & Ryff, 2002, p. 1007). Psychological well-being (PWB) refers to perception of engagement with existential challenges of life (p. 1007). Eudaimonic well-being (EWB) includes feelings of personal expressiveness associated with the pursuit of excellence, virtue, and self-realisation (p. 42; Waterman et al., 2010). Canadian proponents of existential and eudaimonic conceptions of wellbeing include O'Brien (2008), Ryan, Huta, and Deci (2008), and Wong (1998). The most common names of SWB measures are satisfaction and happiness scales, with comparable measures known as affect, morale, or self-reported depression scales (Kozma, Stones, & McNeil, 1991). A Canadian measure used frequently in North America, Europe, and Asia is the Memorial University of Newfoundland Scale of Happiness (MUNSH; Kozma, Stone, Stones, Hannah, & McNeil, 1990; Kozma & Stones, 1980, 1983, 1987, 1988; Kozma, Stones, & Kazarian, 1985; Kozma et al., 1991; Stones & Kozma, 1986a). Along with widely cited measures of satisfaction, such as the Satisfaction with Life Scale (SWLS; Diener, Emmons, Larsen, & Griffin, 1985), evidence for psychometric adequacy is convincing. Measures that fall under Keyes et al.' s (2002) existential challenge rubric include Canadian scales addressing attitudes (Reker & Peacock, 1981), perceived well-being (Reker & Wong, 1984), and personal meaning (Wong, 1998). A widely used battery by Ryff (1989) measures independence and self-determination, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance. …
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".