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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".