The Resource Statistical Model for Multiparty Electoral Data

Statistical Model for Multiparty Electoral Data

Label
Statistical Model for Multiparty Electoral Data
Title
Statistical Model for Multiparty Electoral Data
Creator
Contributor
Author
Contributor
Subject
Summary
In this collection, a comprehensive statistical model for analyzing multiparty, district-level elections is proposed. This model, which provides a tool for comparative politics research analogous to what regression provides in the American two-party context, can be used to explain or predict how geographic distributions of electoral results depend upon economic conditions, neighborhood ethnic compositions, campaign spending, and other features of the election campaign or aggregate areas. Also provided are new graphical representations for data exploration, model evaluation, and substantive interpretation. The authors illustrate the use of this model by attempting to resolve a controversy over the size of and trend in the electoral advantage of incumbency in Britain. Contrary to previous analyses, all based on measures now known to be biased, the research demonstrates that the advantage is small but meaningful, varies substantially across parties, and is not growing. Finally, the authors show how to estimate from which party each other party's advantage is predominantly drawn
http://library.link/vocab/creatorName
  • Katz, Jonathan
  • Inter-university Consortium for Political and Social Research [distributor]
http://library.link/vocab/relatedWorkOrContributorName
King, Gary
Label
Statistical Model for Multiparty Electoral Data
Instantiates
Publication
Note
1190
Control code
ICPSR01190.v1
Governing access note
Access restricted to subscribing institutions
Label
Statistical Model for Multiparty Electoral Data
Publication
Note
1190
Control code
ICPSR01190.v1
Governing access note
Access restricted to subscribing institutions

Library Locations

    • Bowdoin College LibraryBorrow it
      3000 College Station, Brunswick, ME, 04011-8421, US
      43.907093 -69.963997
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