Explanatory and Confirmatory Factors Analysis of Social Well-being Questionnaire
Bibliographic record
Abstract
Introduction: The purpose of present study was to investigate social well-being Questionnaire psychometric properties among university students. Method: To achieve the aim of investigation, undergraduate students among Tabriz University (n=391; 131 male and 260 Female; age Mean= 21.32, SD= 3.12 and 22.42, SD= 2.86 respectively) and Islamic Azad University of Tabriz (n=384; 171 male and 212 Female; age Mean= 21.62, SD= 3.75 and 20.80, SD= 1.49 respectively) were selected by multi-stage cluster sampling and filled Keyes Social Well-being Questionnaire, Memorial University of Newfoundland Scale of Happiness, General Self-efficacy Questionnaire, SCL-90 interpersonal subscale and DASS-42. Results: Explanatory factor analysis showed four factor structure of this questionnaire which could explain 59.70 percent of total variance. Confirmatory factor analysis χ2/df, GFI, AGFI, CFI, AIC and RMSEA indices suggest that five-factor model fit data little more than four-factor model. Also the finding showed social well-being dimensions were positively correlated with happiness and self-efficacy and negatively with difficulties in interpersonal relationship, depression, anxiety and stress. Finally result showed factors Chronbach alpha vary between 0.41 and 0.63 and it was 0.81 for total questionnaire items. Conclusion: Findings corroborate five-factor model of social-well being suggested by Keyes, which imply construct validity of this instrument. Also finding suggest that the social well-being questionnaire has convergent validity and its reliability is good for total but not for factors.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".