Measuring Partisanship as a Social Identity in Multi-Party Systems

Alexa Bankert, Leonie Huddy, Martin Rosema

Research output: Contribution to journalArticleAcademicpeer-review

22 Citations (Scopus)

Abstract

There is no doubt that partisanship is a powerful influence on democratic political behavior. But there is also a lively debate on its nature and origins: Is it largely instrumental in nature and shaped by party performance and issues stances? Or is it basically a long-standing expressive identity reinforced by motivated reasoning and strong emotions? We assess the nature of partisanship in the European context, examining the measurement properties and predictive validity of a multi-item partisan identity scale included in national surveys conducted in the Netherlands, Sweden, and the U.K. Using a latent variable model, we show that an eight-item partisan identity scale provides greater information about partisan intensity than a standard single-item and has the same measurement properties across the three countries. In addition, the identity scale better predicts in-party voting and political participation than a measure of ideological intensity (based on both left–right self-placement and agreement with the party on key issues), providing support for an expressive approach to partisanship in several European democracies.
Original languageEnglish
Pages (from-to)103-132
JournalPolitical behavior
Volume39
Issue number1
DOIs
Publication statusPublished - 2017

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multi-party system
democratic behavior
political behavior
political participation
voting
Sweden
Netherlands
emotion
democracy
performance

Keywords

  • IR-104098
  • METIS-321937

Cite this

Bankert, Alexa ; Huddy, Leonie ; Rosema, Martin. / Measuring Partisanship as a Social Identity in Multi-Party Systems. In: Political behavior. 2017 ; Vol. 39, No. 1. pp. 103-132.
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Measuring Partisanship as a Social Identity in Multi-Party Systems. / Bankert, Alexa; Huddy, Leonie; Rosema, Martin.

In: Political behavior, Vol. 39, No. 1, 2017, p. 103-132.

Research output: Contribution to journalArticleAcademicpeer-review

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