Chapter 18 Poll of polls

Required reading

Required viewing

  • Jackman, Simon, 2020, ‘The triumph of the quants?: Model-based poll aggregation for election forecasting’, Ihaka Lecture Series, https://youtu.be/MvGYsKIsLFs.

Recommended reading

  • Imai, Kosuke, 2017, Quantitative Social Science: An Introduction, Princeton University Press, Ch 4.1, and 5.3.
  • Leigh, Andrew, and Justin Wolfers, 2006, ‘Competing approaches to forecasting elections: Economic models, opinion polling and prediction markets’, Economic Record, 82 (258), pp.325-340.
  • Nickerson, David W., and Todd Rogers, 2014, ‘Political campaigns and big data’, Journal of Economic Perspectives, 28 (2), pp. 51-74.
  • Shirani-Mehr, Houshmand, David Rothschild, Sharad Goel, and Andrew Gelman, 2018, ‘Disentangling bias and variance in election polls’, Journal of the American Statistical Association, 113 (522), pp. 607-614.

Key concepts/skills/etc

Key libraries

Key functions/etc

Quiz

Lab

  • Following the guidance of the TA, please examine various poll-of-polls models, make changes to them, and then see how their output changes.

18.1 Introduction

[The Presidential election of] 2016 was the largest analytics failure in US political history.

David Shor, 13 August 2020

In this section we look at poll-of-polls (equally pooling-the-polls or poll aggregation) approaches which use a statistical model to bring together the outcomes of polls.