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Title: Replication data for: Simulating Models of Issue Voting      
dateReleased:
03-11-2010
downloadURL: http://hdl.handle.net/1902.1/14403
ID:
hdl:1902.1/14403
description:
How should one analyze data when the underlying models being tested are statistically intractable? In this article, we offer a simulation approach that involves creating sets of artificial data with fully known generating models that can be meaningfully compared to real data. The strategy depends on constructing simulations that are well matched to the data against which they will be compared. Our particular concern is to consider concurrently how voters place parties on issue scales and how they evaluate parties based on issues. We reconsider the Lewis and King (2000) analysis of issue voting in Norway. The simulation findings resolve the ambiguity that Lewis and King report, as voters appear to assimilate and contrast party placements and to evaluate parties directionally. The simulations also provide a strong caveat against the use of individually perceived party placements in analyses of issue voting.
description:
Stuart Elaine Macdonald; George Rabinowitz; Ola Listhaug, 2010, "Replication data for: Simulating Models of Issue Voting", http://hdl.handle.net/1902.1/14403, Harvard Dataverse, V1
name:
Stuart Elaine Macdonald
George Rabinowitz
Ola Listhaug
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997