An Evaluation of the Psychometric Properties of the Temporal Satisfaction With Life Scale
Bibliographic record
Abstract
The Temporal Satisfaction with Life Scale measures judgements of life satisfaction using 15 items, according to three temporal dimensions: past, present, and future. However, only seven studies have looked at the psychometric properties of the Temporal Satisfaction with Life Scale, and this has been individually across vastly different countries and cultures (Canada, China, Germany, Spain, Switzerland, Turkey, and United-States), and with different populations, such as undergraduate students, adults, and older adults. In addition, these studies have highlighted issues regarding the replicability of the validity of the scale structure and optimal number of items. In this study we use a large international and multicultural sample (n = 6,912) from the International Wellbeing Study and investigate the scale structure of the Temporal Satisfaction with Life Scale, resulting in the recommendation that a shortened 12-item version provides a better model fit compared to the original 15-item version. More in-depth correlates with aspects of wellbeing and illbeing, in relation to past, present, and future life satisfaction, are also presented than have been previously, which found positive correlations between the temporal dimensions of the Temporal Satisfaction with Life Scale and wellbeing, as well as negative correlations with illbeing measures.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".