1932

Abstract

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.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-031017-100204
2018-03-07
2024-12-12
Loading full text...

Full text loading...

/deliver/fulltext/statistics/5/1/annurev-statistics-031017-100204.html?itemId=/content/journals/10.1146/annurev-statistics-031017-100204&mimeType=html&fmt=ahah

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 [Google Scholar]
  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.. [Google Scholar]
  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/ [Google Scholar]
  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 [Google Scholar]
  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.. [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  17. Dillman DA. 2000. Mail and Internet Surveys: The Tailored Design Method New York: Wiley. , 2nd ed.. [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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/ [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  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/ [Google Scholar]
  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 [Google Scholar]
  40. Mosteller F, Hyman H, McCarthy PJ, Marks ES, Truman DB. 1949. The Pre-Election Polls of 1948 New York: Soc. Sci. Res. Counc. [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  44. Noelle-Neumann E. 1993. The Spiral of Silence: Public Opinion—Our Social Skin Chicago: Univ. Chicago Press, 2nd ed.. [Google Scholar]
  45. Norpoth H. 2016. Primary model predicts Trump victory. The Primary Model Oct. 2016. https://www.primarymodel.com/2016-forecast-full/ [Google Scholar]
  46. Panagakis N. 1989. Incumbent races: closer than they appear. The Polling Report Febr. 27. http://www.pollingreport.com/incumbent.htm [Google Scholar]
  47. Pawlowsky-Glahn V, Buccianti A. 2011. Compositional Data Analysis: Theory and Applications Chichester, UK: John Wiley [Google Scholar]
  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 [Google Scholar]
  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 [Google Scholar]
  56. Silver NA. 2012. The Signal and the Noise: Why So Many Predictions Fail but Some Don't New York: Penguin [Google Scholar]
  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 [Google Scholar]
  61. Traugott MW, Lavrakas PJ. 2008. The Voter's Guide to Election Polls Lanham, MD: Rowman and Littlefield. , 4th ed.. [Google Scholar]
  62. Tufte ER. 1983. The Visual Display of Quantitative Information Cheshire, CT: Graphics Press [Google Scholar]
  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 [Google Scholar]
  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]
/content/journals/10.1146/annurev-statistics-031017-100204
Loading
/content/journals/10.1146/annurev-statistics-031017-100204
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error