PT  - JOURNAL ARTICLE
AU  - Adam Bonica
TI  - A Data-Driven Voter Guide for U.S. Elections: Adapting Quantitative Measures of the Preferences and Priorities of Political Elites to Help Voters Learn About Candidates
AID  - 10.7758/RSF.2016.2.7.02
DP  - 2016 Nov 01
TA  - RSF: The Russell Sage Foundation Journal of the Social Sciences
PG  - 11--32
VI  - 2
IP  - 7
4099  - http://www.rsfjournal.org/content/2/7/11.short
4100  - http://www.rsfjournal.org/content/2/7/11.full
AB  - Internet-based voter advice applications have experienced tremendous growth across Europe in recent years but have yet to be widely adopted in the United States. By comparison, the candidate-centered U.S. electoral system, which routinely requires voters to consider dozens of candidates across a dizzying array of local, state, and federal offices each time they cast a ballot, introduces challenges of scale to the systematic provision of information. Only recently have methodological advances combined with the rapid growth in publicly available data on candidates and their supporters to bring a comprehensive data-driven voter guide within reach. This paper introduces a set of newly developed software tools for collecting, disambiguating, and merging large amounts of data on candidates and other political elites. It then demonstrates how statistical methods developed by political scientists to measure the preferences and expressed priorities of politicians can be adapted to help voters learn about candidates.