Bias Correction by Sub-population Weighting for the 2016 United States Presidential Election

Bumjun Park^1,

¹International Department, Hankuk Academy of Foreign Studies, Yongin, South Korea

Cite this paper

Bumjun Park. Bias Correction by Sub-population Weighting for the 2016 United States Presidential Election. American Journal of Applied Mathematics and Statistics. 2017; 5(3):101-105. doi: 10.12691/AJAMS-5-3-3

Abstract

The 2016 Presidential Election was an international surprise, as President Donald Trump came back from a seemingly large deficit in the pre-election opinion polls. As most, if not all, of the major polls missed the election results, the public started to doubt the credibility of pre-election polls. This article proposes that there was a methodological error in the polls. The polls used the census data of American population to weigh their data. However, population may not have a correlation with turnout, meaning that a certain population group may not vote much; not contributing to the electorate. For this reason, the polls based on population might systematically over or underestimate a particular candidate. Thereby, the proposition is that the polling agencies should consider the electorate, not the population for modifying the polling results. The proposition is substantiated with a series of statistical simulations supporting the claim that a poll conducted based on the electorate resembles the actual result more accurately. Conclusively, it argues that, as the polls play a pivotal role in affecting the media and the electorate, the improvement of polls is necessary for well-informed forecasts to be available.

Keywords

election polls, bias correction, sub-population weighting, turnout rate, simulation, prediction

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	Gelman, A., and King, G., ¡°Why are American presidential election campaign polls so variable when votes are so predictable?¡± British Journal of Political Science, 23 (4), 409-451. Oct. 1993
[2]	Lichtman, J. A., ¡°The keys to the white house: An index forecast for 2008,¡± International Journal of Forecasting, 24 (2), 301-309. Jun. 2008.
[3]	Silver, N. ¡°The Worst Pollster in the World Strikes Again.¡± FiveThirtyEight, Mar. 2009. fivethirtyeight.com/features/worst-pollster-in-world-strikes-again/.
[4]	Hillygus, D. S., ¡°The evolution of election polling in the United States¡±. Public Opinion Quarterly, 75 (5), 962-981. Dec. 2011
[5]	Broh, A. C., ¡°Horse-race journalism: Reporting the polls in the 1976 presidential election,¡± Public Opinion Quarterly, 44 (4), 514-529. Jan. 1980.
[6]	Mosteller, F., The Pre-election Polls of 1948: The Report to the Committee on Analysis of Pre-election Polls and Forecasts. Social Science Research Council, New York, 1949.
[7]	Shipman, J., & Leve, J. H., ¡°A New "Interval" Measure of Election Poll Accuracy,¡± Paper presented at the annual meeting of the American Association For Public Opinion Association. May. 2005.
[8]	Burden, B. C., ¡°Voter turnout and the national election studies,¡± Political Analysis, 8 (4), 389-398. Jul. 2000.
[9]	Reiter, H. L., ¡°Why is turnout down?¡± Public Opinion Quarterly, 43 (3), 297-311. Jan. 1979.
[10]	Shaffer, S. D., ¡°A multivariate explanation of decreasing turnout in presidential elections 1960-1976¡±, American Journal of Political Science, 25 (1), 68-95. Feb. 1981.
[11]	Ladd, E. C., ¡°The Shifting Party Coalitions, 1932-1976,¡± Emerging Coalitions in American Politics, 81-102, Jan. 1978.