Demystifying the Election Prediction 2016 Contest

ASA is sponsoring the Prediction 2016 contest, giving students a chance to take part in this year’s election season by using statistics to predict the next president of the United States. Think you’re up for the challenge? We’ve compiled a few resources to help you get started.

FiveThirtyEight

Nate Silver, a well-known American statistician, offers some great tips on how to create good prediction models on his website, FiveThirtyEight. He suggests models be probabilistic and empirical while also responding sensibly to change in inputs and avoiding changes of rules in midstream. There is also a very helpful “User’s Guide” on FiveThirtyEight’s prediction process for the 2016 election that explains the four major steps Silver follows.

Polling Data

So where’s a good first stop on the journey to predicting the next U.S. president? Polls. Data need to be collected, weighted and averaged before any models can be created or any predictions can be made. There are several different places on the web where election polling data can be found, including: Gallup, Quinnipiac and RealClearPolitics, among others. It’s also important to adjust and combine polls with other data points and account for uncertainty when making predictions.

Statistical Methods

After polling data have been collected and adjusted, it’s time to choose which statistical methods will be used to predict the election. This is an opportunity to get creative, as there are several different methods that can be used. Biostatistician Bob O’Hara wrote an article speculating how Nate Silver correctly predicted the outcome of every state in the 2012 U.S. presidential election, suggesting Silver used Bayes’ Theorem, graph theory and hierarchical modeling to make his predictions.

Jo Hardin, professor of mathematics at Pomona College, offers a different possible approach in her post on R-statistics blog. Hardin provides an analysis of data that uses weighted means and a formula for the standard error of a weighted mean. She also suggests the possibility of using a generalized linear model.

Finally, it may be worth taking a look at the Time for Change forecasting model for some additional inspiration. This model estimates the weights of just three predictors and has correctly predicted every U.S. presidential election since 1988.

Additional Resources

For additional reading on statistical approaches for election prediction, see the following:

Forsberg OJ, and Payton ME. (2015) “Analysis of Battleground State Presidential Polling Performances, 2004–2012,” Statistics and Public Policy, 2, 1-10. (Source)

Christensen WF, and Florence LW. (2008) “Predicting presidential and other multistage election outcomes using state-level pre-election polls,” The American Statistician, 62, 1-10. (Source)

Park, D. K., Gelman, A., and Bafumi, J. (2004), “Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls,” Political Analysis, 12, 375–385. (Source)

Facebooktwittergoogle_plusredditpinterestlinkedinmail

Related Posts

Hessney+Pic

Meet Sharon Hessney, the Educator Behind the New York Times Learning Network’s “What’s Going On In This Graph?”

Sharon Hessney is an award-winning mathematics teacher in Boston and graph curator for the New York Times Learning Network’s “What’s Going In This Graph?” feature. She gave This is Statistics an in-depth look into her work and advice for students looking to start careers in the statistics field. Who inspired you  to work in statistics education? The Advanced Placement Statistics community of experienced statistics teachers. AP Statistics emphasizes…

0 comments
EYzfTikXkAAd5cM

Congrats to Our 2020 #StatsGrad Winner

Thanks to all the students, parents and teachers who celebrated 2020 graduates with us by entering the June #StatsGrad contest!   We’ve enjoyed looking through the your messages and videos submitted during our 2020 #StatsGrad contest. We’re excited to announce Erin Bugbee as this year’s winner!     Erin received her Bachelor of Science degree with honors in statistics and Bachelor of Arts degree in behavioral decision sciences from Brown University. She is excited to continue her studies at Carnegie Mellon University as a behavioral decision…

0 comments

Comments are closed.