This chapter examines how attitudes are formed. Attitude formation is explained as a function of prior beliefs and information. This process is viewed through two complementary lenses: the static process and the dynamic process. The static model thinks of attitudes as a combination of ratings and rankings. We term this the multi-attribute model – a commonly used approach in psychology and economics. The dynamic model concentrates on how humans process information, where things like words, symbols, and memory networks take on practical significance. Ultimately, both models have many applications for the practitioner.

This chapter covers the concepts of error and bias and their application in practice using a total error framework. This includes a discussion of how to manage both sampling and non-sampling error, and covers ways to assess and address coverage bias, nonresponse bias, measurement error, and estimation error.

This chapter focuses on critical concepts that underlie our conceptualization of public opinion, including the link between the public and those who govern, public opinion’s stability, opinion as an attitude, and convergence. Pollsters need to understand these concepts to do their job properly. This chapter seeks to answer the questions: Why is public opinion important? Is it stable? and What is the role of emotions in opinion formation?

In this chapter, we define a communication strategy for the 2022 Brazilian presidential election using public opinion inputs. We ask a simple question – what is the winning message?

To do this, we deploy polling results from three 2,000 interview face-to-face polls and a battery of focus groups. These are what we call a benchmark, designed to identify key message themes and other public opinion inputs. To assess the campaign in course, we will analyze about 40,900 interviews conducted during 152 days of tracking. Note that we did not work for any campaign in Brazil. But we polled for private sector clients who wanted to understand and predict the election. In that capacity, we used our polling to mimic campaign dynamics in order to assess their relative effectiveness.

This chapter applies the total error framework presented in Chapter 5 to a case example of preelection polling during the 2016 US presidential election. Here, the focus is on problems with a single poll.

This chapter applies the total error framework presented in Chapter 5 to a case example of aggregate polls in the 2015 Greek referendum. The focus here is on why the polls in aggregate predicted the wrong outcome.

This chapter provides an overview of the arguments for the stability of public opinion as well as the arguments of those who believe public opinion is unstable. We then explore conditionality, or the conditions under which public opinion appears to be more unstable. Concepts such as low information rationality, issue salience, and question wording are covered.

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.