The growing and progressive decrease in voter turnout affects almost all established democracies and all types of electoral consultations, albeit with different intensities. The aim of the paper is to identify which cultural and socio-economic factors may contribute to explain the voter turnout in the 2022 Italian parliamentary election. The determinants of non-voting are identified using data from the European Social Survey through a graphical modeling approach based on Bayesian networks.
Marella, D., Musella, F., Vicard, P. (2026). Data-Driven Analysis of No-Vote Rates Using Bayesian Networks. In Statistical Science: From Theory to Applied Research IV. SIS-FENStatS 2026. Springer [10.1007/978-3-032-30665-4_18].
Data-Driven Analysis of No-Vote Rates Using Bayesian Networks
Musella F.
;Vicard P.
2026-01-01
Abstract
The growing and progressive decrease in voter turnout affects almost all established democracies and all types of electoral consultations, albeit with different intensities. The aim of the paper is to identify which cultural and socio-economic factors may contribute to explain the voter turnout in the 2022 Italian parliamentary election. The determinants of non-voting are identified using data from the European Social Survey through a graphical modeling approach based on Bayesian networks.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
