The state-trait model of cheerfulness: Tests of measurement invariance and latent mean differences in European and Chinese Canadian students
Bibliographic record
Abstract
The State-Trait Cheerfulness Inventory (STCI) assesses latent traits and states of cheerfulness, seriousness, and bad mood to represent the temperamental basis of humor. The present study (1) tested the generalizability of the three-factor model in both state and trait versions of the STCI across European Canadian (N = 489) and first generation Chinese Canadian (N = 147) participants completing the English version of the STCI and (2) compared latent mean differences. Results indicated the confirmatory factor analyses of the three-factor model for European White participants born in Canada and Chinese participants born in China showed adequate fit for both trait and state measures. Furthermore, substantial equivalence of factor model parameters and partial scalar invariance were found for both the state and trait STCI measures. In examining latent mean differences, European White Canadian participants reported significantly higher trait cheerfulness, z = 3.30, p < .001, d = 0.84, and lower trait bad mood z = 3.25, p < .01, d = 0.80 compared to the Chinese Canadian groups. European White Canadian participants reported significantly lower state bad mood, z = 3.59, p < .001, d = 1.15, compared to the Chinese Canadian groups. Limitations and future directions based on study findings are discussed.
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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 | 0.002 | 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 | 0.000 | 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".