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Article: Closeness Counts: Increasing Precision and Reducing Errors in Mass Election Predictions

TitleCloseness Counts: Increasing Precision and Reducing Errors in Mass Election Predictions
Authors
Issue Date2015
PublisherOxford University Press. The Journal's web site is located at http://pan.oxfordjournals.org/
Citation
Political Analysis, 2015, v. 23, p. 518-533 How to Cite?
AbstractMass election predictions are increasingly used by election forecasters and public opinion scholars. While they are potentially powerful tools for answering a variety of social science questions, existing measures are limited in that they ask about victors rather than voteshares. We show that asking survey respondents to predict voteshares is a viable and superior alternative to asking them to predict winners. After showing respondents can make sensible quantitative predictions, we demonstrate how traditional qualitative forecasts lead to mistaken inferences. In particular, qualitative predictions vastly overstate the degree of partisan bias in election forecasts, and lead to wrong conclusions regarding how political knowledge exacerbates this bias. We also show how election predictions can aid in the use of elections as natural experiments, using the effect of the 2012 election on partisan economic perceptions as an example. Our results have implications for multiple constituencies, from methodologists and pollsters to political scientists and interdisciplinary scholars of collective intelligence.
Persistent Identifierhttp://hdl.handle.net/10722/229413

 

DC FieldValueLanguage
dc.contributor.authorQuek, CK-
dc.contributor.authorSances, MW-
dc.date.accessioned2016-08-23T14:10:59Z-
dc.date.available2016-08-23T14:10:59Z-
dc.date.issued2015-
dc.identifier.citationPolitical Analysis, 2015, v. 23, p. 518-533-
dc.identifier.urihttp://hdl.handle.net/10722/229413-
dc.description.abstractMass election predictions are increasingly used by election forecasters and public opinion scholars. While they are potentially powerful tools for answering a variety of social science questions, existing measures are limited in that they ask about victors rather than voteshares. We show that asking survey respondents to predict voteshares is a viable and superior alternative to asking them to predict winners. After showing respondents can make sensible quantitative predictions, we demonstrate how traditional qualitative forecasts lead to mistaken inferences. In particular, qualitative predictions vastly overstate the degree of partisan bias in election forecasts, and lead to wrong conclusions regarding how political knowledge exacerbates this bias. We also show how election predictions can aid in the use of elections as natural experiments, using the effect of the 2012 election on partisan economic perceptions as an example. Our results have implications for multiple constituencies, from methodologists and pollsters to political scientists and interdisciplinary scholars of collective intelligence.-
dc.languageeng-
dc.publisherOxford University Press. The Journal's web site is located at http://pan.oxfordjournals.org/-
dc.relation.ispartofPolitical Analysis-
dc.rightsPre-print: Journal Title] ©: [year] [owner as specified on the article] Published by Oxford University Press [on behalf of xxxxxx]. All rights reserved. Pre-print (Once an article is published, preprint notice should be amended to): This is an electronic version of an article published in [include the complete citation information for the final version of the Article as published in the print edition of the Journal.] Post-print: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in [insert journal title] following peer review. The definitive publisher-authenticated version [insert complete citation information here] is available online at: xxxxxxx [insert URL that the author will receive upon publication here]. -
dc.titleCloseness Counts: Increasing Precision and Reducing Errors in Mass Election Predictions-
dc.typeArticle-
dc.identifier.emailQuek, CK: quek@hku.hk-
dc.identifier.authorityQuek, CK=rp01797-
dc.identifier.doi10.1093/pan/mpv022-
dc.identifier.hkuros260388-
dc.identifier.volume23-
dc.identifier.spage518-
dc.identifier.epage533-

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