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Campaign Learning and Vote Determinants
Author(s) -
Peterson David A. M.
Publication year - 2009
Publication title -
american journal of political science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.347
H-Index - 170
eISSN - 1540-5907
pISSN - 0092-5853
DOI - 10.1111/j.1540-5907.2009.00380.x
Subject(s) - markov chain , politics , aggregate (composite) , measure (data warehouse) , logit , markov chain monte carlo , function (biology) , political science , econometrics , psychology , economics , computer science , monte carlo method , statistics , mathematics , database , machine learning , composite material , materials science , law , evolutionary biology , biology
How campaigns shape voters' decisions is central to the study of political behavior. The basic conclusion is simple: campaigns matter. While we know who campaigns influence, there is no clear empirical evidence of why or how campaigns matter. This comes from two things. First, despite different theories about campaigns, the existing studies measure the campaign as a function of time. Second, these studies ignore the individual‐level psychological mediators of these effects. We know that there are differences across time during a campaign, but we do not know how or why. In this article I suggest that campaigns work by altering voters' uncertainty about the candidates and combine aggregate and individual‐level data using a hierarchical logit estimated via Markov chain Monte Carlo methods. I find that voters change how they weigh their attitudes during the campaign because of changes in their uncertainty about the candidates.