Socioeconomic factors, including social attitudes and values, play a crucial role in assessing subjective happiness. This study explores the strength and the structure of the relationships between subjective happiness and various relational factors, alongside other variables considered relevant within happiness theory, using Bayesian network (BN) analysis. These associations were initially identified in a previous study by the authors, which employed Path Analysis on the same data from the Bank of Italy’s 2016 SHIW Survey. The present work enables a direct comparison between the two methodologies in evaluating causal relationships. Additionally, a data-driven model — where the direction of the links between relational factors and subjective happiness remained unconditioned — was tested to address the issue of reverse causality, as discussed in the literature. BN analysis, with its flexibility in capturing nonlinear and probabilistic associations, enhances this examination. This model is further applied to subgroups based on gender and marital status to assess its robustness. Although the BN analysis and the earlier Path model yield similar findings, their comparison offers a more comprehensive understanding of the relational factors that most influence subjective happiness and the mechanisms through which they operate.
ISTAT, Italian National Institute of Statistics, Italy - ORCID: 0000-0001-7764-2261
Ca' Foscari University of Venice, Italy - ORCID: 0000-0002-4204-1009
Titolo del capitolo
Exploring subjective happiness and relational factors: A Bayesian network and path analysis comparison
Autori
Alessandra Righi, Debora Slanzi
Lingua
Inglese
DOI
10.36253/979-12-215-1062-1.62
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Titolo del libro
Data, Statistics and AI for Well-Being of People and Organisations
Curatori
Luigi Fabbris, Alberto Franci, Pietro Renzi
Opera sottoposta a peer review
Numero di pagine
420
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-1062-1
ISBN Print
979-12-215-1061-4
eISBN (pdf)
979-12-215-1062-1
eISBN (xml)
979-12-215-1063-8
Collana
Applied Statistics Series