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Increasing Precision and Reducing Errors in Mass Election Predictions

Bibliographic Data

ID6332115
AuthorsKai Quek (0000-0001-8403-4280, University of Hong Kong), Michael W Sances (0000-0003-0013-3738, University of Memphis, corresponding author)
Year2015
Volume23
Issue4
Pages518-533
Publication date2015-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePolitical Analysis (JOURNAL)
Journal identifiersISSN: 1047-1987 • E-ISSN: 1476-4989
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1093/pan/mpv022
OpenAlexW2259992910
LanguageEN
Citations received1
References cited35

Mass 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

Closeness · Economics · Natural experiment · Perception · Political science · Politics · Positive economics · Public opinion · Public relations · Statistics · Variety (cybernetics · Computer Science · Electoral Systems and Political Participation · Media Influence and Politics · Psychology · Sports Analytics and Performance · Law

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Unique citing works1
Citations per year0,11
Citation span2017 - 2017 (1)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 1

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