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Record W4317743268 · doi:10.3390/ejihpe13020019

Cultural Differences in How People Deal with Ridicule and Laughter: Differential Item Functioning between the Taiwanese Chinese and Canadian English Versions of the PhoPhiKat-45

2023· article· en· W4317743268 on OpenAlexafffundabout
Chloé Lau, Taylor Swindall, Francesca Chiesi, Lena C. Quilty, Hsueh‐Chih Chen, Yu‐Chen Chan, Willibald Ruch, René T. Proyer, Francesco Bruno, Donald H. Saklofske, Jorge Torres‐Marín

Bibliographic record

VenueEuropean Journal of Investigation in Health Psychology and Education · 2023
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsWestern UniversityCentre for Addiction and Mental Health
FundersMitacsCanadian Institutes of Health ResearchMental Health Research Canada
KeywordsLaughterDifferential item functioningPsychologyChinaSocial psychologyChinese peopleMeasurement invarianceDevelopmental psychologyItem response theoryConfirmatory factor analysisPsychometricsStructural equation modelingStatisticsHistoryMathematics

Abstract

fetched live from OpenAlex

The PhoPhiKat-45 measures three dispositions toward ridicule and laughter, including gelotophobia (i.e., the fear of being laughed at), gelotophilia (i.e., the joy of being laughed at), and katagelasticism (i.e., the joy of laughing at others). Despite numerous cultural adaptations, there is a paucity of cross-cultural studies investigating measurement invariance of this measure. Undergraduate students from a Canadian university (N = 1467; 71.4% females) and 14 universities in Taiwan (N = 1274; 64.6% females) completed the English and Chinese PhoPhiKat-45 measures, respectively. Item response theory and differential item functioning analyses demonstrated that most items were well-distributed across the latent continuum. Five of 45 items were flagged for DIF, but all values had negligible effect sizes (McFadden’s pseudo R2 < 0.13). The Canadian sample was further subdivided into subsamples who identified as European White born in Canada (n = 567) and Chinese born in China, Hong Kong, or Taiwan (n = 180). In the subgroup analyses, no evidence of DIF was found. Findings support the utility of this measure across these languages and samples.

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)0.001
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 score0.041
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.035
GPT teacher head0.316
Teacher spread0.281 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

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