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Can Registration-Based Sampling Improve the Accuracy of Midterm Election Forecasts

Bibliographic Data

ID6370965
AuthorsDonald P Green (0000-0002-8850-438X, corresponding author)
Year2006
Volume70
Issue2
Pages197-223
Publication date2006-06-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenuePublic Opinion Quarterly (JOURNAL)
Journal identifiersISSN: 0033-362X • E-ISSN: 1537-5331
PublisherOxford University Press (OUP) (PUBLISHER)
DOI10.1093/poq/nfj022
OpenAlexW2027750460
LanguageEN
Citations received12
References cited3

We compare the predictive accuracy of preelection polls using two types of sampling frames, random digit dialing (RDD) and registration-based sampling (RBS). The latter involves stratified random sampling from voter registration lists. In order to assess the accuracy with which RDD and RBS predict election out? comes, we collaborated with the Washington Post, Quinnipiac, and CBS News polls, which conducted parallel RDD and RBS surveys in Maryland, New York, Pennsylvania, and South Dakota prior to the November 5, 2002, elections. The results suggest that in the guber- natorial and congressional elections studied, RBS performed as well, if not better, than RDD, both in terms of forecasting accuracy and cost. Each election year features an implicit competition between polls using two different approaches to sampling. Media, commercial, and academic pollsters rely almost exclusively on random digit dialing (RDD). This sampling method directs calls to randomly generated telephone numbers within certain area codes and exchanges (Groves 1990). Pollsters for political campaigns, on the

Advertising · Business · Competition (biology · Population · Random digit dialing · Sampling (signal processing · Sociology · Statistics · Stratified sampling · Telecommunications · Telephone number · Computer Science · Electoral Systems and Political Participation · Mathematics · Media Influence and Politics · Survey Methodology and Nonresponse · Demography

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Unique citing works12
Citations per year0,6
Citation span2006 - 2024 (19)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 12

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