Election polls, also called election surveys, have been under severe criticism because of apparent gaps between their outcomes and election results. In this article, we survey election poll performance in the United States, United Kingdom, Canada, and Israel and discuss the current state of the art. We list the main data collection methods used in election surveys, describe a wide range of analysis techniques that can be applied to such data, and expand on the relatively new application of predictive models used in this context. A special section considers sources of error in election surveys followed by an introduction and a general discussion of an information quality framework for studying them. We conclude with a section on outlooks and proposals that require more research.


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Literature Cited

  1. AAPOR (Am. Assoc. Public Opin. Res.). 2010. Research synthesis: AAPOR report on online panels.. Pub. Opin. Q. 74:4711–81 [Google Scholar]
  2. ACE Elect. Knowl. Netw. 2006. Comparative Data Summary http://aceproject.org/epic-en
  3. Anderson B, Silver B, Abramson P. 1988. The effects of the race of the interviewer on race-related attitudes of black respondents in SRC/CPS national election studies. Pub. Opin. Q. 52:3289–324 [Google Scholar]
  4. Asher H. 2007. Polling and the Public: What Every Citizen Should Know Washington, DC: CQ Press. , 7th ed..
  5. Bialik C, Enten H. 2016. The polls missed Trump. We asked pollsters why. FiveThirtyEight Nov. 9. http://fivethirtyeight.com/features/the-polls-missed-trump-we-asked-pollsters-why/
  6. Blumenthal MM. 2005. Toward an open-source methodology: what we can learn from the blogosphere. Pub. Opin. Q. 69:5655–69 [Google Scholar]
  7. Ceron A, Curini L, Iacus SM. 2014. Social Media e Sentiment Analysis: L'evoluzione dei fenomeni sociali attraverso la Rete Sxl—Springer for Innovation Ser. 9 New York: Springer
  8. Ceron A, Curini L, Iacus SM. 2015. iSA: a fast, scalable and accurate algorithm for sentiment analysis of social media content. Inf. Sci. 368:1105–24 [Google Scholar]
  9. Chambers R. 1999. Probability sampling. Model Quality Report in Business Statistics P Davies, P Smith 7–39 Newport, UK: Off. Natl. Stat https://users.soe.ucsc.edu/∼draper/bergdahl-etal-1999-v1.pdf [Google Scholar]
  10. Cleveland WS. 1994. The Elements of Graphing Data Summit, NJ: Hobart. , 2nd ed..
  11. Confessore N, Hakim D. 2017. Data firm says ‘secret sauce’ aided Trump; many scoff. New York Times Mar. 6 A1 https://www.nytimes.com/2017/03/06/us/politics/cambridge-analytica.html?referer=https://t.co/7VjOulz9Q5&_r=1
  12. Curini L, Iacus SM, Canova L. 2015. Measuring idiosyncratic happiness through the analysis of twitter: an application to the Italian case. Soc. Indic. Res. 121:2525–42 [Google Scholar]
  13. Dalla Valle L, Kenett RS. 2015. Official statistics data integration to enhance information quality. Qual. Reliab. Eng. Int. 31:71281–300 [Google Scholar]
  14. Daves RP. 2000. Who will vote? Ascertaining likelihood to vote and modeling a probable electorate in pre-election polls. Election Polls, the News Media and Democracy P Lavrakas, M Traugott 205–23 New York: Chatham House [Google Scholar]
  15. Deville JC, Särndal CE. 1992. Calibration estimators in survey sampling. J. Am. Stat. Assoc. 87:376–82 [Google Scholar]
  16. Dillman DA. 1978. Mail and Telephone Surveys New York: Wiley
  17. Dillman DA. 2000. Mail and Internet Surveys: The Tailored Design Method New York: Wiley. , 2nd ed..
  18. Erikson RS, Panagopoulos C, Wlezien C. 2004. Likely (and unlikely) voters and the assessment of campaign dynamics. Pub. Opin. Q. 68:4588–601 [Google Scholar]
  19. Erikson RS, Sigelman L. 1995. Poll-based forecasts of midterm congressional elections: Do the pollsters get it right?. Pub. Opin. Q. 59:589–605 [Google Scholar]
  20. Farmer B. 2015. Independent inquiry announced into what went wrong with election polls. The Telegraph May 8. http://www.telegraph.co.uk/news/general-election-2015/11592840/Independent-inquiry-announced-into-what-went-wrong-with-election-polls.html
  21. Frankovic K, Panagopoulos C, Shapiro RY. 2009. Opinion and election polls. Handbook of Statistics: Sample Surveys: Inference and Analysis D Pfeffermann, CR Rao 567–95 Amsterdam: North-Holland [Google Scholar]
  22. Fricker S, Galesic M, Tourangeau R, Yan T. 2005. An experimental comparison of web and telephone surveys. Pub. Opin. Q. 69:3370–92 [Google Scholar]
  23. Fuchs C. 2017. Surveys and election forecasts in a world of social media and party dealignment. The Elections in Israel 2015 M Shamir, G Rahat, pp. 213–36 New Brunswick, NJ: Transaction [Google Scholar]
  24. Gelman A, Pasarica C, Dodhia R. 2002. Let's practice what we preach: turning tables into graphs. Am. Stat. 56:121–30 [Google Scholar]
  25. Grajales C. 2015. So what can we learn from Nate Silver's mistakes. StatisticsViews May 14. http://www.statisticsviews.com/details/news/7945151/So-what-can-we-learn-from-Nate-Silvers-mistakes.html
  26. Groves RM, Lyberg L. 2010. Total survey error: past, present and future. Pub. Opin. Q. 74:849–79 [Google Scholar]
  27. Heckman JJ. 1979. Sample selection bias as a specification error. Econometrica 47:153–61 [Google Scholar]
  28. Kellner P. 2015. We got it wrong. Why?. YouGov UK May 11. https://yougov.co.uk/news/2015/05/11/we-got-it-wrong-why/
  29. Kenett RS. 1991. Two methods for comparing Pareto charts. J. Qual. Technol. 23:27–31 [Google Scholar]
  30. Kenett RS, Salini S. 2011. Modern Analysis of Customer Satisfaction Surveys: With Applications Using R Chichester, UK: John Wiley
  31. Kenett RS, Shmueli G. 2014. On information quality. J. R. Stat. Soc. A 177:3–38 [Google Scholar]
  32. Kenett RS, Shmueli G. 2016. Information Quality: The Potential of Data and Analytics to Generate Knowledge Chichester, UK: John Wiley
  33. Kohavi R, Longbotham R, Sommerfield D, Henne RM. 2009. Controlled experiments on the web: survey and practical guide. Data Min. Knowl. Discov. 18:140–81 [Google Scholar]
  34. Lai KKR, Parlapiani A, White J, Yourish K. 2016. How Trump won the election according to exit polls. New York Times Nov. 8. http://www.nytimes.com/interactive/2016/11/08/us/elections/exit-poll-analysis.html
  35. Lauderdale B. 2015. What we got wrong in our 2015 U.K. general election model. FiveThirtyEight May 8. https://fivethirtyeight.com/datalab/what-we-got-wrong-in-our-2015-uk-general-election-model/
  36. Lee S, Valliant R. 2009. Estimation for volunteer panel web surveys using propensity score adjustment and calibration adjustment. Sociol. Methods Res. 37:319–43 [Google Scholar]
  37. Lohr SL, Raghunathan TE. 2017. Combining survey data with other data sources. Stat. Sci. 32:2293–312 [Google Scholar]
  38. McAllister I, Studlar DT. 1991. Bandwagon, underdog, or projection? Opinion polls and electoral choice in Britain, 1979–1987. J. Polit. 53:720–41 [Google Scholar]
  39. Mercer A, Deane C, McGeeney K. 2016. Why 2016 election polls missed their mark. Fact Tank Nov. 9. http://www.pewresearch.org/fact-tank/2016/11/09/why-2016-election-polls-missed-their-mark
  40. Mosteller F, Hyman H, McCarthy PJ, Marks ES, Truman DB. 1949. The Pre-Election Polls of 1948 New York: Soc. Sci. Res. Counc.
  41. MSNBC. 2016. Michael Moore joins wide-ranging election talk. Morning Joe Nov. 11. http://www.msnbc.com/morning-joe/watch/michael-moore-joins-wide-ranging-election-talk-806604867876
  42. Nathan G. 2001. Telesurvey methodologies for household surveys—a review and some thoughts on the future. Surv. Methodol. 27:7–37 [Google Scholar]
  43. Natl. Acad. Sci. Eng. Med. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy Washington, DC: Natl. Acad. Press
  44. Noelle-Neumann E. 1993. The Spiral of Silence: Public Opinion—Our Social Skin Chicago: Univ. Chicago Press, 2nd ed..
  45. Norpoth H. 2016. Primary model predicts Trump victory. The Primary Model Oct. 2016. https://www.primarymodel.com/2016-forecast-full/
  46. Panagakis N. 1989. Incumbent races: closer than they appear. The Polling Report Febr. 27. http://www.pollingreport.com/incumbent.htm
  47. Pawlowsky-Glahn V, Buccianti A. 2011. Compositional Data Analysis: Theory and Applications Chichester, UK: John Wiley
  48. Pfeffermann D. 2013. New important developments in small area estimation. Stat. Sci. 28:40–68 [Google Scholar]
  49. Pfeffermann D. 2015. Methodological issues and challenges in the production of official statistics: 24th Annual Morris Hansen Lecture. J. Surv. Stat. Methodol. 3:4425–83 [Google Scholar]
  50. Pfeffermann D, Landsman V. 2011. Are private schools really better than public schools? Assessment by methods for observational studies. Ann. Appl. Stat. 5:1726–51 [Google Scholar]
  51. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:141–55 [Google Scholar]
  52. Rubin DB. 1987. Multiple Imputation for Nonresponse in Surveys New York: Wiley
  53. Saris W. 1998. Ten years of interviewing without interviewers: the telepanel. Computer Assisted Survey Information Collection NP Couper, RP Baker, J Bethlehem, CZF Clark, J Martin et al.409–31 New York: Wiley [Google Scholar]
  54. Schonlau M, Couper MP. 2017. Options for conducting web surveys. Stat. Sci. 32:2279–92 [Google Scholar]
  55. Shmueli G, Bruce PC, Stephens ML, Patel NR. 2017. Data Mining for Business Analytics Hoboken, NJ: John Wiley
  56. Silver NA. 2012. The Signal and the Noise: Why So Many Predictions Fail but Some Don't New York: Penguin
  57. Simon HA. 1954. Bandwagon and underdog effects and the possibility of election predictions. Pub. Opin. Q. 18:245–53 [Google Scholar]
  58. Smith TW. 1978. In search of house effects: a comparison of responses to various questions by different survey organization. Pub. Opin. Q. 42:443–63 [Google Scholar]
  59. Smith TW. 1990. The first straw: a study of the origins of election polls. Pub. Opin. Q. 54:21–36 [Google Scholar]
  60. Sturgis P, Baker N, Callegaro M, Fisher S, Green J. et al. 2016. Report of the inquiry into the 2015 British general election opinion polls London: Br. Poll. Counc. and Mark. Res. Soc http://eprints.ncrm.ac.uk/3789/1/Report_final_revised.pdf
  61. Traugott MW, Lavrakas PJ. 2008. The Voter's Guide to Election Polls Lanham, MD: Rowman and Littlefield. , 4th ed..
  62. Tufte ER. 1983. The Visual Display of Quantitative Information Cheshire, CT: Graphics Press
  63. Visser P, Krosnick J, Marquette J, Curtin M. 2000. Improving election forecasting: allocation of undecided respondents, identification of likely voters and response order effects. Election Polls, the News Media and Democracy P Lavrakas, M Traugott 224–60 New York: Chatham House [Google Scholar]
  64. Vives-Mestres M, Martín-Fernández J-A, Kenett RS. 2016. Compositional data methods in customer survey analysis. Qual. Reliab. Eng. Int. 32:62115–25 [Google Scholar]
  65. Washington Post. 2004. Daily tracking poll methodology. Washington Post Nov. 1. http://www.washingtonpost.com/wp-dyn/articles/A9363-2004Oct5.html
  66. Yahav I, Shmueli G, Mani D. 2016. A tree-based approach for addressing self-selection in impact studies with big data. Man. Inf. Syst. Q. 40:4819–48 [Google Scholar]

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