Forecasting elections is a high-risk, high-reward endeavor. Today’s polling rock star is tomorrow’s has-been. It is a high-pressure gig. Public opinion polls have been a staple of election forecasting for almost ninety years. But single source predictions are an imperfect means of forecasting, as we detailed in the preceding chapter. One of the most telling examples of this in recent years is the 2016 US presidential election. In this chapter, we will examine public opinion as an election forecast input. We organize election prediction into three broad buckets: (1) heuristics models, (2) poll-based models, and (3) fundamentals models.

To wit, we have three specific goals here. First, we want to review the activities of the three-hatted pollster. We do this to provide greater context for each type of pollster. Some of us are all three; others are some combination of these. Any pollster worth their salt must at least be a data scientist, or they risk losing credibility.

Second, we explore the role of the pollster in society. Ultimately, what is the purpose of the pollster? In our view, pollsters are critically important in any democracy. We believe this is often overlooked due to the ranking frenzy after every electoral cycle. Here, we put the profession into proper perspective.

And third, we discuss the use of non-survey, or alternative data, inputs as proxy measures for public opinion. We provide a framework for pollsters to think through them in a critical manner. Validation is a key concept which we introduce here – one more tool for the data scientist.

This chapter provides an overview of the purpose of the book, namely to help the user of public opinion data develop a systematic analytical approach for understanding, predicting, and engaging public opinion. This includes helping the reader understand how public opinion can be employed as a decision-making input, meaning a factor, or variable, to assess, predict, or influence an outcome. The chapter outlines how information from different disciplines, including cognitive psychology, behavioral economics, and political science, come together to inform the pollster’s work.

This chapter tackles two additional activities of the pollster as fortune teller. The first is the assessment and prediction of government approval ratings. As we have already seen in Chapter 8, approval ratings are extremely important in predicting elections. There is both an art and science to the analysis of such measures. Here, we want to lay out an analytical framework which will allow pollsters to assess both structural and policy factors related to approval ratings and then how to utilize multiple methods to triangulate future outcomes. We will focus on the Biden administration circa August 2022. Ultimately, a fairly large component of a pollster’s workload is the continual assessment of government initiatives and their convergence (or not) with what people want.

The second is a discussion of more context-based analysis. The pollster has an important role in helping decision-makers understand the bigger picture. Here, broader demographic and social trends help gird such analysis.

In this chapter, we discuss both the structural and the packaging perspectives in conceptual terms. It is worth noting that the communications literature is diffuse and poorly integrated. Some of it reads more like self-help books. To be fair, it does draw on many different disciplines – some more rigorous; others less so. As such, our purpose here is to provide a clear framework for the pollster and practitioner. There is considerable art and creativity to effective communications. Look at Cannes Lion every year- the Oscars of the PR and Marketing world. There is incredible creativity in the crafting of impactful messages. But public opinion is public opinion – with a few basic compositional truths. By nailing them down, the pollster is able to provide structure to the communications process.

Predictions often falter because of human error. Most misses have much more to do with our own human shortcomings than with the technical sophistication of the method at hand. In our experience, forecasting errors occur when we discard or misinterpret evidence right in front of us. The clues are there, but we are blinded by our own filters. This is why it is essential to tackle such biases and discuss corresponding solutions. In this chapter, we’ll look at studies on the forecasting prowess of experts. Then, we’ll focus on cognitive biases that skew predictions. Finally, we’ll present an applied approach to minimize such biases.

This book provides statistics instructors and students with complete classroom material for a one- or two-semester course on applied regression and causal inference. It is built around 52 stories, 52 class-participation activities, 52 hands-on computer demonstrations, 52 drills, and 52 discussion problems that allow instructors and students to explore in a fun way the real-world complexity of the subject. The book fosters an engaging “flipped classroom” environment with a focus on visualization and understanding.

The book provides instructors with frameworks for self-study or for structuring the course, along with tips for maintaining student engagement at all levels and practice exam questions to help guide learning.

Designed to accompany the authors’ previous textbook Regression and Other Stories, its modular nature and wealth of material allow this book to be adapted to different courses and texts or to be used by learners as a hands-on workbook.

The authors are experienced researchers who have published articles in hundreds of different scientific journals in fields including statistics, computer science, policy, public health, political science, economics, sociology, and engineering. They have also published articles in the Washington Post, the New York Times, Slate, and other public venues. Their previous books include Bayesian Data Analysis, Teaching Statistics: A Bag of Tricks, and Regression and Other Stories.

This book provides statistics instructors and students with complete classroom material for a one- or two-semester course on applied regression and causal inference. It is built around 52 stories, 52 class-participation activities, 52 hands-on computer demonstrations, 52 drills, and 52 discussion problems that allow instructors and students to explore in a fun way the real-world complexity of the subject. The book fosters an engaging “flipped classroom” environment with a focus on visualization and understanding.

The book provides instructors with frameworks for self-study or for structuring the course, along with tips for maintaining student engagement at all levels and practice exam questions to help guide learning.

Designed to accompany the authors’ previous textbook Regression and Other Stories, its modular nature and wealth of material allow this book to be adapted to different courses and texts or to be used by learners as a hands-on workbook.

The authors are experienced researchers who have published articles in hundreds of different scientific journals in fields including statistics, computer science, policy, public health, political science, economics, sociology, and engineering. They have also published articles in the Washington Post, the New York Times, Slate, and other public venues. Their previous books include Bayesian Data Analysis, Teaching Statistics: A Bag of Tricks, and Regression and Other Stories.