Psychometric Evaluation of the Chinese Version of the Subjective Happiness Scale: Evidence from the Hong Kong FAMILY Cohort
Bibliographic record
Abstract
BACKGROUND: With China's rapid economic growth in the past few decades, there is currently an emerging focus on happiness. Cross-cultural validity studies have indicated that the four-item Subjective Happiness Scale (SHS) has high internal consistency and stable reliability. However, the psychometric characteristics of the SHS in broader Chinese community samples are unknown. PURPOSE: We evaluated the factor structure and psychometric properties of the SHS in the Hong Kong general population. METHODS: The Chinese SHS was derived using forward-backward translation. Of the Cantonese-speaking participants aged ≥15 years, 2,635 were randomly selected from the random sample component of the FAMILY Cohort, a territory-wide cohort study in Hong Kong. In addition to the SHS, a single-item overall happiness scale, the Patient Health Questionnaire-9 (PHQ-9), the Family Adaptation, Partnership, Growth, Affection, Resolve (APGAR) scale, and the Medical Outcomes Study 12-item short-form version 2 (SF-12) mental and physical health scales were administered. RESULTS: Exploratory and confirmatory factor analyses supported a single factor with high loadings for the four SHS items. Multiple group analyses indicated factor invariance across sex and age groups. Cronbach's alpha was 0.82, and 2-week test-retest reliability (n = 191) was 0.70. The SHS correlated significantly with single-item overall happiness (Spearman's rho [ρ] = 0.57), Family APGAR (ρ = 0.26), PHQ-9 (ρ = -0.34), and mental health-related quality of life (ρ = 0.40) but showed a lower correlation with physical health (ρ = 0.15). A regression model that included the PHQ-9 and Family APGAR scores explained 37% of the variance in SF-12 mental health scores; adding the SHS raised the variance explained to 41 %. CONCLUSIONS: Our results support the reliability and validity of the SHS as a relevant component in the measurement battery for mental well-being in a Chinese general population.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".