What's Your Humor Profile? A Latent Profile Analysis on the State-Trait Model of Cheerfulness as the Temperamental Basis of Humor

被引：0

作者：

Lau, Chloe ^{[1
]}

Li, Catherine ^{[2
,6
]}

Swindall, Taylor ^{[2
,8
]}

Chiesi, Francesca ^{[3
]}

Ruch, Willibald ^{[4
]}

Bruno, Francesco ^{[5
]}

Saklofske, Donald H. H. ^{[2
]}

Quilty, Lena C. C. ^{[1
,7
]}

机构：

[1] Ctr Addict & Mental Hlth, Toronto, ON, Canada

[2] Univ Western Ontario, Dept Psychol, London, ON, Canada

[3] Univ Florence, Dept Neurosci Psychol Drug & Childs Hlth NEUROFARB, Sect Psychol, Florence, Italy

[4] Univ Zurich, Dept Psychol, Personal & Assessment, Zurich, Switzerland

[5] ASP Catanzaro, Reg Neurogenet Ctr CRN, Dept Primary Care, Viale A Perugini, Lamezia Terme, CZ, Italy

[6] Northwestern Univ, Dept Med Social Sci, Chicago, IL USA

[7] Univ Toronto, Dept Psychiat, Toronto, ON, Canada

[8] Brock Univ, Dept Nursing, St Catharines, ON, Canada

来源：

JOURNAL OF HAPPINESS STUDIES | 2023年 / 24卷 / 06期

基金：

加拿大健康研究院;

关键词：

Humor; Cheerfulness; Serious; Bad mood; Laughter; Person-centered; SENSE; CONSTRUCTION; PERSONALITY; INVENTORY; STRESS; FORM;

D O I：

10.1007/s10902-023-00671-1

中图分类号：

B84 [心理学];

学科分类号：

04 ; 0402 ;

摘要：

The temperamental basis of humor posits that high cheerfulness, low seriousness, and low bad mood contribute to exhilaration and enjoyment of humor. The present study extends the empirical support for this model in an examination of whether different latent profiles exist based on levels of cheerfulness, seriousness, and bad mood. Latent profile analysis was conducted in a sample of 788 undergraduate participants (71.6% female) 16 to 40 years old (M = 18.28; SD = 1.24) to evaluate humor temperament subtypes based on facets of cheerfulness, seriousness, and bad mood. Boosting classification machine learning (ML) algorithm was employed to examine classes using broad personality traits and humor-related variables. Results supported four profiles labeled temperamental basis of humor, humorlessness, Homo Ludens, and disengagement. Based on Bayesian ANOVAs, the temperamental profile had the highest scores in extraversion, agreeableness, and sense of humor. ML findings showed gelotophobia (Relative Influence [RI] = 33.09), using humor in everyday life (RI = 19.88), and laughter (RI = 11.56) were better predictors of profiles than broad personality traits. Findings provide evidence for different humor profiles, and enable more personalized assessments to better understand and evaluate how different temperamental traits affect the expression of humor.

引用

页码：2061 / 2081

页数：21

共 11 条

[11] What Can Happen When We Look at Art?: An Exploratory Network Model and Latent Profile Analysis of Affective/Cognitive Aspects Underlying Shared, Supraordinate Responses to Museum Visual Art
Miller, Stephanie
Cotter, Katherine N.
Fingerhut, Joerg
Leder, Helmut
Pelowski, Matthew
EMPIRICAL STUDIES OF THE ARTS, 2024,

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