An evaluation of the factor structure of the Self-Assessed Wisdom Scale (SAWS) and the creation of the SAWS-15 as a short measure for personal wisdom
Bibliographic record
Abstract
OBJECTIVES: Although wisdom is a desirable life span developmental goal, researchers have often lacked brief and reliable construct measures. We examined whether an abbreviated set of items could be empirically derived from the popular 40-item five-factor Self-Assessed Wisdom Scale (SAWS). DESIGN: Survey data from 709 respondents were randomly split into two and analyzed using confirmatory factor analysis (CFA). SETTING: The survey was conducted online in Australia. PARTICIPANTS: The total sample consisted of 709 participants (Mage = 35.67 years; age range = 15-92 years) of whom 22% were male, and 78% female. MEASUREMENT: The study analyzed the 40-item SAWS. RESULTS: Sample 1 showed the traditional five-factor structure for the 40-item SAWS did not fit the data. Exploratory factor analysis (EFA) on Sample 2 offered an alternative model based on a 15-item, five-factor solution with the latent variables Reminiscence/Reflection, Humor, Emotional Regulation, Experience, and Openness. This model, which replicates the factor structure of the original 40-item SAWS with a short form of 15 items, was then confirmed on Sample 1 using a CFA that produced acceptable fit and measurement invariance across age groups. CONCLUSIONS: We suggest the abbreviated SAWS-15 can be useful as a measure of individual differences in wisdom, and we highlight areas for future research.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".