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Exploring subjective happiness and relational factors: A Bayesian network and path analysis comparison

  • Alessandra Righi
  • Debora Slanzi

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.

  • Keywords:
  • Disability,
  • Ageing,
  • Continuous-time Markov model,
  • Interval-censored data,
  • Semiparametric estimation,
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Alessandra Righi

ISTAT, Italian National Institute of Statistics, Italy - ORCID: 0000-0001-7764-2261

Debora Slanzi

Ca' Foscari University of Venice, Italy - ORCID: 0000-0002-4204-1009

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  • Anno di pubblicazione: 2026

Informazioni sul capitolo

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

CC BY 4.0

Licenza dei metadati

CC0 1.0

Informazioni bibliografiche

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

CC BY 4.0

Licenza dei metadati

CC0 1.0

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Firenze University Press

DOI

10.36253/979-12-215-1062-1

ISBN Print

979-12-215-1061-4

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979-12-215-1062-1

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979-12-215-1063-8

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Applied Statistics Series

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