The literature highlights how different individual levels of political interest and knowledge matter for political attitudes and behavior. A logical-quantitative voting model is thus proposed for a two-party system, based on voters' left–right ideological positions and their degree of political involvement. The model hypothesizes that although more involved voters generally behave in accordance with their ideological orientation, those who are less involved do not. Moreover, the latter tend to be more undecided and therefore likely to be more strongly influenced by campaign activities. This model is then applied to survey data regarding the 2001 Italian general elections. Results confirm the hypotheses and show that the most competitive area is ideologically a narrow centrist area for very involved citizens, becoming wider as the level of involvement decreases. Separate analyses are carried out for different geopolitical areas of the country, with results fitting the political history of these areas.

Segnalazione bibliografica. British Journal of Political Science (2011), 41: 287-314 Autori: David Sanders, Harold D. Clarke, Marianne C. Stewart, Paul Whiteley Abstract A six-wave 2005–09 national panel survey conducted in conjunction with the British Election Study provided data for an investigation of sources of stability and change in voters’ party preferences. The authors test competing spatial and valence theories of party choice and investigate the hypothesis that spatial calculations provide cues for making valence judgements. Analyses reveal that valence mechanisms – heuristics based on party leader images, party performance evaluations and mutable partisan attachments – outperform a spatial model in terms of strength of direct effects on party choice....

Multidimensional scaling (or MDS) is a methodology for producing geometric models of proximities data. Multidimensional scaling has a long history in political science research. However, most applications of MDS are purely descriptive, with no attempt to assess stability or sampling variability in the scaling solution. In this article, we develop a bootstrap resampling strategy for constructing confidence regions in multidimensional scaling solutions. The methodology is illustrated by performing an inferential multidimensional scaling analysis on data from the 2004 American National Election Study (ANES). The bootstrap procedure is very simple, and it is adaptable to a wide variety of MDS models. Our approach enhances the utility of multidimensional scaling as a tool for testing substantive theories while still retaining the flexibility in assumptions, model details, and estimation procedures that make MDS so useful for exploring structure in data.