Positive emotion dispositions and emotion regulation in the Italian population

Alice Chirico, Michelle N. Shiota, Andrea Gaggioli

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The goal of this large-scale study was to test the relationship between positive emotion dispositions (i.e., Joy, Contentment, Pride, Love, Compassion, Amusement, and Awe) and two strategies of emotion regulation (i.e., reappraisal and suppression) in the Italian population. 532 Italian-speaking adults completed the Dispositional Positive Emotion Scales (DPES), the Positive and Negative Affective Schedule (PANAS), the Italian Emotion Regulation Questionnaire (ERQ), and the Big-Five Inventory (BFI). DPES scales showed high reliability. Exploratory Factor Analysis showed that a 6-factor model fits the Italian sample better. Joy and Contentment loaded on the same factor. Items assessing the other five emotions loaded on separate factors. The patterns of relationships between positive emotion dispositions, positive and negative affects traits (PANAS), and personality traits (BFI) indicated concurrent validity of the DPES. Twelve separated multiple regression models with BFI and ERQ factors as predictors and DPES factors as response variables showed that Extraversion significantly positively predicted of all DPES emotions. Agreeableness predicted Happiness, Love, Compassion, and Awe positively. Conscientiousness predicted Amusement and Love negatively and Compassion, Pride, and Happiness positively. Neuroticism predicted all emotions negatively except for Compassion. Positive emotions were significantly and positively predicted by reappraisal, and negatively predicted by suppression.

Original languageEnglish (US)
Article numbere0245545
JournalPloS one
Volume16
Issue number3 March
DOIs
StatePublished - Mar 2021

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'Positive emotion dispositions and emotion regulation in the Italian population'. Together they form a unique fingerprint.

Cite this