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Record W4417355246 · doi:10.1111/sjop.70049

Psychometric Analysis and Cross‐Cultural Comparisons of the Italian and English Sense of Humor Scale Parallel Version Short Form

2025· article· en· W4417355246 on OpenAlexafffundabout
Chloé Lau, Willibald Ruch, Sonja Heintz, Lena C. Quilty, Francesco Bruno, Donald H. Saklofske, Francesca Chiesi

Bibliographic record

VenueScandinavian Journal of Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthWestern University
FundersMitacsCanadian Institutes of Health ResearchMental Health Research Canada
KeywordsDifferential item functioningScale (ratio)Reliability (semiconductor)PsychometricsShort FormsItem response theorySense of humorItem analysis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.383
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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