Public Opinion Quarterly Advance Access published online on March 19, 2009
Public Opinion Quarterly, doi:10.1093/poq/nfp004
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Pre-Election Polling: Identifying Likely Voters Using Iterative Expert Data Mining
Address correspondence to Gregg R. Murray; e-mail: g.murray{at}ttu.edu.
One often-noted difficulty in pre-election polling is the identification of likely voters. Our objective is to build a likely voter model for presidential elections that efficiently balances accuracy and number of questions used. We employ the Iterative Expert Data Mining technique and data from the American National Election Studies to identify a small number of survey questions that can be used to classify likely voters while maintaining or surpassing the accuracy rates of other models. Specifically, we propose two survey items that together correctly classify 78 percent of respondents as voters or nonvoters over a multielection, multidecade period. We argue that our proposed model compares favorably to competing models by capturing the successful elements of those models while ignoring other elements that constrain identification. We end by suggesting that our model offers a new approach to identifying and evaluating likely voters that may maintain or increase accuracy without also increasing cost.
GREGG R. MURRAY is with the Department of Political Science, Texas Tech University, Box 41015, Lubbock, TX 79409, USA. CHRIS RILEY is with the Department of Political Science, Binghamton University, PO Box 6000, Binghamton, NY 13902, USA. ANTHONY SCIME is with the Department of Computer Science, The College at Brockport, State University of New York, Brockport, NY 14420, USA. We are grateful for the helpful comments of Dena Levy, Kulathur S. Rajasethupathy, and the anonymous reviewers. We are especially grateful for the financial support of Andrea Ciliotta-Rubery.