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Unsicherheiten der Wählerinnen und Wähler bei der Wahrnehmung des Politikangebots der Parteien. Beschreibung für die Bundestagswahl 2017 und Modellierung als Teil der Wahlfunktion

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Abstract

Wähler und Wählerinnen unterscheiden sich in der Sicherheit ihres politischen Urteils. Unterschiedlich sichere Urteile über Parteipositionen auf vorgegebenen Issue- und Ideologie-Skalen haben nach der Entscheidungstheorie unter Risiko Folgen für die Wahlentscheidung nach der Policy- oder ideologischen Nähe zu den Parteien. So wird eine risikoaverse Wählerin von zwei Parteien, die sie als gleich nahe zu ihrer Idealposition wahrnimmt, die Partei wählen, über deren Position sie sich ein sichereres Urteil zutraut. Wir stellen ein innovatives Verfahren zur Messung der themenbezogenen Wählerunsicherheiten beim Urteil über Parteipositionen vor und wenden es auf die Links-Rechts-Skala und die sozioökonomische, Umweltschutz- und Migrationsfragen des Vorwahlquerschnitts der GLES 2017 an.

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... In addition to these distinct influences of distance and heterogeneity on coalition preferences, an interaction between them is conceivable. The reasoning behind this assumption arises from two lines of research: First, research on the role of uncertainty concludes that the utility of a party for a voter depends not only on policy distances but also on how certain a voter is about the position of the respective party; if a riskaverse voter is about to choose between two equally distant parties, they vote for the party whose position they are more certain about (see Bartels 1986;Gill 2005;Pappi and Bräuninger 2021). Second, previous research shows increased relevance of valence when candidates or parties ideologically converge (see, e.g., Green and Hobolt 2008;Franchino and Zucchini 2015). ...
... Drawing from research using similar data on previous federal elections, it is possible that a significant share of respondents were not able to address or didn't know about the parties' positions on the issue dimensions (see, e.g., Giebler et al. 2021;Pappi and Bräuninger 2021). To avoid estimation problems due to listwise deletion, I followed the approach by Giebler et al. (2021) and used hot deck imputation for missing data on the placements of the parties. ...
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