The validity of well-being measures: A multiple-indicator–multiple-rater model.
Bibliographic record
Abstract
In the subjective indicators tradition, well-being is defined as a match between an individual's actual life and his or her ideal life. Common well-being indicators are life-satisfaction judgments, domain satisfaction judgments, and measures of positive and negative affect (hedonic balance). These well-being indicators are routinely used to study well-being, but a formal measurement model of well-being is lacking. This article introduces a measurement model of well-being and examines the validity of self-ratings and informant ratings of well-being. Participants were 335 families (1 student with 2 parents, N = 1,005). The main findings were that (a) self-ratings and informant ratings are equally valid, (b) global life-satisfaction judgments and averaged domain satisfaction judgments are about equally valid, and (c) about 1/3 of the variance in a single indicator is valid. The main implication is that researchers should demonstrate convergent validity across multiple indicators by multiple raters.
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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 | 0.344 | 0.520 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".