Can Registration-Based Sampling Improve the Accuracy of Midterm Election Forecasts?
DONALD P. GREEN and ALAN S. GERBER are professors of political science at Yale University. Earlier versions of this report were presented at the 2002 annual meeting of the American Association for Public Opinion Research, Nashville, TN, and the Gallup Conference on Improving the Accuracy of Polling, May 2–4, 2002, Washington, DC.
Address correspondence to Donald P. Green; e-mail: donald.green{at}yale.edu.
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 outcomes, 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 gubernatorial and congressional elections studied, RBS performed as well, if not better, than RDD, both in terms of forecasting accuracy and cost.
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