This paper presents a selection of findings drawn from a national research project carried out in Italy and aiming to identify the strategies that Italian voters enact in order to combine political information originating from exposure to election campaigns and information processed during social interaction, in the wider context of the Italian political system. New fields of research have focused on socio-cognitive factors that affect voting choices and information search processes undertaken to formulate judgments via cognitive shortcuts (or “heuristics”). In particular, this project extends the scope of an innovative voting decision model developed by Richard R. Lau and David P. Redlawsk and adapts it to the Italian context. The voting decision model is implemented via a “dynamic information board” simulating election campaigns tailored to observe information search strategies in which voters engage. This technique employs a controlled-environment, on-line simulation, endeavouring to reproduce a complex, realistic situation, in which the information that the voter can access changes over time. This article, in particular, focuses on the operationalization of so-called “correct voting”, i.e., the voting behaviour that voters would enact if they operated in conditions of complete information, and the divergence between the final voting decision expressed within the simulation and “correct” votes. Section 1 comprises a short overview of the concept and the operationalizations of “voting correctly”. Section 2 briefly addresses the platform used for simulating the election campaign and describes the research project. Section 3 outlines specific features of the Italian political system that influenced some operationalization and analysis choices, which are then implemented in Sections 4 and 5, dedicated, respectively, to illustrating key variables and developing a series of models through multinomial logistic regression. The final section contains some concluding remarks.
Gasperoni, G., Mantovani, D. (2013). Assessing Correct Voting: A Study Based on A Simulation of Municipal Elections in Italy. STATISTICA APPLICATA, 25(2), 139-164.
Assessing Correct Voting: A Study Based on A Simulation of Municipal Elections in Italy
GASPERONI, Giancarlo;MANTOVANI, DEBORA
2013
Abstract
This paper presents a selection of findings drawn from a national research project carried out in Italy and aiming to identify the strategies that Italian voters enact in order to combine political information originating from exposure to election campaigns and information processed during social interaction, in the wider context of the Italian political system. New fields of research have focused on socio-cognitive factors that affect voting choices and information search processes undertaken to formulate judgments via cognitive shortcuts (or “heuristics”). In particular, this project extends the scope of an innovative voting decision model developed by Richard R. Lau and David P. Redlawsk and adapts it to the Italian context. The voting decision model is implemented via a “dynamic information board” simulating election campaigns tailored to observe information search strategies in which voters engage. This technique employs a controlled-environment, on-line simulation, endeavouring to reproduce a complex, realistic situation, in which the information that the voter can access changes over time. This article, in particular, focuses on the operationalization of so-called “correct voting”, i.e., the voting behaviour that voters would enact if they operated in conditions of complete information, and the divergence between the final voting decision expressed within the simulation and “correct” votes. Section 1 comprises a short overview of the concept and the operationalizations of “voting correctly”. Section 2 briefly addresses the platform used for simulating the election campaign and describes the research project. Section 3 outlines specific features of the Italian political system that influenced some operationalization and analysis choices, which are then implemented in Sections 4 and 5, dedicated, respectively, to illustrating key variables and developing a series of models through multinomial logistic regression. The final section contains some concluding remarks.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.