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.
What’s Your Humor Profile? A Latent Profile Analysis on the State-Trait Model of Cheerfulness as the Temperamental Basis of Humor
Bruno F.;
2023-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
