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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".