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Voter Registration Databases and MRP: Toward the Use of Large-Scale Databases in Public Opinion Research

Published online by Cambridge University Press:  20 March 2020

Yair Ghitza*
Affiliation:
Catalist, 1310 L St. NW, Suite 500, Washington, DC20005, USA. Email: [email protected]
Andrew Gelman
Affiliation:
Columbia University, Department of Political Science, 1255 Amsterdam Avenue, Room 1003, New York, NY10027, USA

Abstract

Declining telephone response rates have forced several transformations in survey methodology, including cell phone supplements, nonprobability sampling, and increased reliance on model-based inferences. At the same time, advances in statistical methods and vast amounts of new data sources suggest that new methods can combat some of these problems. We focus on one type of data source—voter registration databases—and show how they can improve inferences from political surveys. These databases allow survey methodologists to leverage political variables, such as party registration and past voting behavior, at a large scale and free of overreporting bias or endogeneity between survey responses. We develop a general process to take advantage of this data, which is illustrated through an example where we use multilevel regression and poststratification to produce vote choice estimates for the 2012 presidential election, projecting those estimates to 195 million registered voters in a postelection context. Our inferences are stable and reasonable down to demographic subgroups within small geographies and even down to the county or congressional district level. They can be used to supplement exit polls, which have become increasingly problematic and are not available in all geographies. We discuss problems, limitations, and open areas of research.

Type
Articles
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press on behalf of the Society for Political Methodology.

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Footnotes

Contributing Editor: Jonathan Nagler

References

AAPOR Cell Phone Task Force. 2010. “New Considerations for Survey Researchers when Planning and Conducting RDD Telephone Surveys in the U.S. with Respondents Reached via Cell Phone Numbers.” Prepared for AAPOR Council by the Cell Phone Task Force operating under the auspices of the AAPOR Standards Committee.Google Scholar
AAPOR Task Force. 2013. “Report of the AAPOR Task Force on Non-Probability Sampling.” Working Paper.Google Scholar
Ansolabehere, S., and Hersh, E.. 2012. “Validation: What Big Data Reveal About Survey Misreporting and the Real Electorate.” Political Analysis 20(4):437459.CrossRefGoogle Scholar
Barreto, M. A., Guerra, F., Marks, M., Nuño, S. A., and Woods, N. D.. 2006. “Controversies in Exit Polling: Implementing a Racially Stratified Homogeneous Precinct Approach.” PS: Political Science & Politics 39(3):477483.Google Scholar
Barreto, M. A., Segura, G. M., and Woods, N. D.. 2004. “The Mobilizing Effect of Majority-Minority Districts on Latino Turnout.” American Political Science Review 98(1):6575.CrossRefGoogle Scholar
Campbell, A., Converse, P. E., Miller, W. E., and Stokes, D. E.. 1964. The American Voter . New York: Wiley.Google Scholar
Coppock, A., and Green, D. P.. 2016. “Is Voting Habit Forming? New Evidence from Experiments and Regression Discontinuities.” American Journal of Political Science 60(4):10441062.CrossRefGoogle Scholar
Duane, S., Kennedy, A. D., Pendleton, B. J., and Roweth, D.. 1987. “Hybrid Monte Carlo.” Physics Letters B 195(2):216222.CrossRefGoogle Scholar
Enos, R. D., and Fowler, A.. 2014. The Effects of Large-Scale Campaigns on Voter Turnout: Evidence from 400 Million Voter Contacts. Unpublished manuscript, Harvard University.Google Scholar
Erikson, R. S., Panagopoulos, C., and Wlezien, C.. 2004. “Likely (and Unlikely) Voters and the Assessment of Campaign Dynamics.” Public Opinion Quarterly 68(4):588601.10.1093/poq/nfh041CrossRefGoogle Scholar
Fraga, B. L. 2016. “Candidates or Districts? Reevaluating the Role of Race in Voter Turnout.” American Journal of Political Science 60(1):97122.CrossRefGoogle Scholar
Gelman, A., and Hill, J.. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models . New York: Cambridge University Press.Google Scholar
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B.. 2004. Bayesian Data Analysis . Boca Raton, FL: Chapman and Hall/CRC.Google Scholar
Ghitza, Y., and Gelman, A.. 2013. “Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups.” American Journal of Political Science 57(3):762776.CrossRefGoogle Scholar
Ghitza, Y., and Gelman, A.. 2019. “Replication Data for: Voter Registration Databases and MRP: Toward the Use of Large Scale Databases in Public Opinion Research.” doi:10.7910/DVN/H9X2AB, Harvard Dataverse, V1, UNF:6:PRdwfPnZN/+X+RTkDmOdpQ== [fileUNF].Google Scholar
Green, D. P., and Gerber, A. S.. 2006. “Can Registration-Based Sampling Improve the Accuracy of Midterm Election Forecasts? Public Opinion Quarterly 70(2):197223.CrossRefGoogle Scholar
Hersh, E. D., and Schaffner, B. F.. 2013. “Targeted Campaign Appeals and the Value of Ambiguity.” The Journal of Politics 75(02):520534.10.1017/S0022381613000182CrossRefGoogle Scholar
Hersh, E. D., and Nall, C.. 2016. “The Primacy of Race in the Geography of Income-Based Voting: New Evidence from Public Voting Records.” American Journal of Political Science 60(2):289303.CrossRefGoogle Scholar
Hoffman, M. D., and Gelman, A.. 2014. “The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.” Journal of Machine Learning Research 15:13511381.Google Scholar
Hur, A., and Achen, C. H.. 2013. “Coding Voter Turnout Responses in the Current Population Survey.” Public Opinion Quarterly 77(4):985993.CrossRefGoogle Scholar
Issenberg, S.2012a. “How Obama’s Team Used Big Data to Rally Voters.” MIT Technology Review, December 19. https://www.technologyreview.com/s/509026/how-obamas-team-used-big-data-to-rally-voters/.Google Scholar
Issenberg, S. 2012b. The Victory Lab: The Secret Science of Winning Campaigns . New York: Random House.Google Scholar
Jackman, S., and Spahn, B.. 2015. “Unlisted in America.” Unpublished paper.Google Scholar
Lamb, A., Fuller, M., Varadarajan, R., Tran, N., Vandiver, B., Doshi, L., and Bear, C.. 2012. “The Vertica Analytic Database: C-Store 7 Years Later.” Proceedings of the VLDB Endowment 5(12):17901801.CrossRefGoogle Scholar
Malchow, H. 2008. Political Targeting . Washington, DC: Campaigns and Elections.Google Scholar
Mann, C. B., and Klofstad, C. A.. 2015. “The Role of Call Quality in Voter Mobilization: Implications for Electoral Outcomes and Experimental Design.” Political Behavior 37(1):135154.CrossRefGoogle Scholar
McDonald, M. P. 2007. “The True Electorate: A Cross-Validation of Voter Registration Files and Election Survey Demographics.” Public Opinion Quarterly 71(4):588602.CrossRefGoogle Scholar
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E.. 1953. “Equation of State Calculations by Fast Computing Machines.” The Journal of Chemical Physics 21(6):10871092.10.1063/1.1699114CrossRefGoogle Scholar
Olivella, S., and Montgomery, J. M.. 2018. “Tree-based models for political Science Data.” American Journal of Political Science 62(3):729744.Google Scholar
R Core Team. 2012. R: A Language and Environment for Statistical Computing . Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/.Google Scholar
Rao, J. N. K. 2005. Small Area Estimation . New York: Wiley.Google Scholar
Rivers, D.2007. “Sampling for Web Surveys.” White paper prepared from presentation given at the 2007 Joint Statistical Meetings, Salt Lake City, Utah, July–August. https://pdfs.semanticscholar.org/fffa/a7e52c5d163a0944974a68160ee6e0a6b481.pdf.Google Scholar
Rogers, T., and Aida, M.. 2011. “Why Bother Asking? The Limited Value of Self-Reported Vote Intention.” HKS Working Paper RWP12-001.10.2139/ssrn.1971846CrossRefGoogle Scholar
Stan Development Team. 2013. “Stan: A C++ Library for Probability and Sampling.” http://mc-stan.org/, Version 1.3.Google Scholar
Waksberg, J. 1978. “Sampling Methods for Random Digit Dialing.” Journal of the American Statistical Association 73(361):4046.CrossRefGoogle Scholar
Wang, W., Rothschild, D., Goel, S., and Gelman, A.. 2015. “Forecasting Elections with Non-Representative Polls.” International Journal of Forecasting 31(3):980991.10.1016/j.ijforecast.2014.06.001CrossRefGoogle Scholar