Psychometric Analysis and Cross‐Cultural Comparisons of the Italian and English Sense of Humor Scale Parallel Version Short Form
Bibliographic record
Abstract
ABSTRACT The Sense of Humor Scale parallel version short form (SHS‐PSF) is a novel self‐report measure aimed at describing personality traits related to enjoyment of humor, laughter, verbal humor, humor under stress, humor in everyday life, and laughing at oneself. The present study recruited Italian ( N = 298) and Canadian ( N = 910) participants to complete the Italian and English versions, respectively, to assess the measurement properties of the newly translated Italian SHS‐PSF together with Canadian results. The bifactor and six‐factor models show more optimal fit indices than the one‐factor model, albeit insufficient incremental validity indices. Based on Samejima's graded response model, item discrimination parameters ranged from 0.32 to 2.58 (median = 1.24), with 27 of 29 items showing moderate to very high discrimination parameters. Conditional reliability estimates reveal accurate measurements across the latent continuum. Four items had uniform differential item functioning (DIF) when comparing the Italian and English SHS‐PSF (McFadden's pseudo R 2 > 0.035 or β > 0.10). The Italian SHS‐PSF has insufficient‐to‐acceptable psychometric properties. Cross‐language measurement evaluation comparisons suggest significant biases in 4 of 29 items using conservative DIF approaches.
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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 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".