Development and Validation of the Short Version of the Sense of Humor Scale (SHS-S)
Bibliographic record
Abstract
Abstract. Humor training has become increasingly popular to enhance the “sense of humor” and well-being and to decrease depressive symptoms. Despite the wide applications of these training programs, the assessment of training efficacy has attracted less attention. The Sense of Humor Scale (SHS; McGhee, 1996 , 1999 ) recently was expanded to a long version (SHS-L) to enhance its internal consistency ( Ruch & Heintz, 2018 ). At the same time, there is also the need for a brief version of this scale. The purpose of the present study is to develop a short version (SHS-S) in both German- and English-speaking countries, test its psychometric properties (internal consistency, factorial, construct, and criterion validity), and assess measurement invariance across gender and the two languages. Using three samples (Sample 1: 570 English-speakers, Sample 2: 353 German-speakers, Sample 3: 94 other-reports), the 29-item SHS-S was developed and yielded promising internal consistency and validity scores for the six humor skill factors of enjoyment of humor, laughter, verbal humor, finding humor in everyday life, laughing at yourself, and humor under stress. Overall, the SHS-S is an internally consistent, valid, and economic tool for future research and group-based applications, while the SHS-L seems especially useful in individual applications.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".