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

Kai Quek and Michael W. Sances

Political Analysis, 2015, vol. 23, issue 4, 518-533

Abstract: 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.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:23:y:2015:i:04:p:518-533_01

Access Statistics for this article

More articles in Political Analysis from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().