The state-trait model of cheerfulness and social desirability: an investigation on psychometric properties and links with well-being
Bibliographic record
Abstract
Abstract Ruch and colleagues (Ruch, Willibald, Gabriele Köhler & Christoph Van Thriel. 1996. Assessing the “humorous temperament”: Construction of the facet and standard trait forms of the state-trait-cheerfulness-inventory — STCI. Humor 9(3–4). 303–340) postulated high cheerfulness, low seriousness, and low bad mood contribute to exhilaration and enjoyment of humor. Although robust findings have corroborated that cheerfulness is associated with well-being and greatly enhances one’s social desirability, no studies have investigated the effects of social desirability on the assessment of cheerfulness. For this study, 997 undergraduate students completed the State-Trait Cheerfulness Inventory (STCI) and validity measures. Exploratory factor analyses that controlled for social desirability suggest several items on the STCI cheerfulness subscale loaded on social desirability, whereas seriousness subscale items showed few positive loadings on social desirability and bad mood subscale items loaded negatively on social desirability. Despite associations with social desirability, items overall showed strong loadings onto their respective factors. Factor loadings free of social desirability ranged from 0.39 to 0.84 in cheerfulness, 0.49 to 0.76 in seriousness, and 0.50 to 0.81 in bad mood. Cheerfulness, seriousness, and bad mood subscale scores demonstrated partial correlations in the expected directions with well-being when controlling for social desirability, albeit smaller in size but not significantly different. The STCI scores demonstrated strong psychometric properties with good reliability, structural validity, and criterion validity when controlling for social desirability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".