Variability of Coefficient Alpha: An Empirical Investigation of the Scales of Psychological Wellbeing
Bibliographic record
Abstract
Using reliability generalization analysis, the purpose of this study was to characterize the average score reliability, the variability of the score reliability estimates, and explore possible characteristics (e.g., sample size) that influence the reliability of scores across studies using the Scales of Psychological Wellbeing (PWB; Ryff, 1989 , 2014 ). Published studies were included in this investigation if they appeared in a peer-reviewed journal, used 1 or more PWB subscales, estimated coefficient alpha value(s) for the PWB subscale(s), and were written in English. Of the 924 articles generated by the search strategy, a total of 264 were included in the final sample for meta-analysis. The average value reported for coefficient alpha referencing the composite PWB Scale was 0.858, with mean coefficient alphas ranging from 0.722 for the autonomy subscale to 0.801 for the self-acceptance subscale. The 95% prediction intervals ranged from [.653, .996] for the composite PWB. The lower bound of the prediction intervals for specific subscales were >.350. Moderator analyses revealed significant differences in score reliability estimates across select sample and test characteristics. Most notably, R2 values linked with test length ranged from 40% to 71%. Concerns were identified with the use of the 3-item per PWB subscale which reinforces claims advanced by Ryff (2014) . Suggestions for researchers using the PWB are advanced which span measurement considerations and standards of reporting. Psychological researchers who calculate score reliability estimates within their own work should recognize the implications of alpha coefficient values on validity, null hypothesis significant testing, and effect sizes.
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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 | 0.231 | 0.517 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".