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.

This chapter uses history of polling to explain how pollsters have dealt with challenges of nonresponse. It tells the tale of three polling paradigms: large-scale polling, quota sampling, and random sampling. The first two paradigms came crashing down after pollsters made poor predictions for presidential elections. The third paradigm remains vibrant intellectually, but is increasingly difficult to implement. We do not yet know if the bad polling predictions in 2016 and 2020 will push the field to a new paradigm, but certainly they raised doubts about the current state of the field.

This chapter focuses on next-generation selection models that allow us to expand on the Heckman model using copula and control function models that allow one to estimate selection models for a large range of other statistical distributions. This chapter also shows how to generate weights that account for nonignorable nonresponse; not only do these weights increase the weight on demographic groups that respond with lower probabilities, they also increase weights on people with opinions that may make them less inclined to respond. This chapter also shows how to modify a Heckman model to allow for estimation of a nonignorable nonresponse selection model when we have a response-related variable that is available only for people in the survey sample.

This is a brief conclusion arguing that the direction forward is clear, even if the path is not. The time for assuming away problems is past. We should begin with a paradigm that reflects all the ways that polling can go wrong and then identify, model, and measure all the sources of bias, not just the ones that are easy to fix. Much work remains to be done, though, as these new models and data sources will require much evaluation and development theoretically, empirically, and practically. The payoff will be that survey researchers will be able to remain true to their aspirations of using information about a small number of people to understand the realities about many people, even as it gets harder and hear from anyone, let alone the random samples that our previous theory relied on.

This chapter explores ways to diagnose the potential for nonignorable nonresponse to cause problems. Section 7.1 describes how to define the range of possible values of population values that are consistent with the observed data. These calculations require virtually no assumptions and are robust to nonignorable nonresponse; they are simple yet tend to be uninformative. Section 7.2 shows how to postulate possible levels of nonignorability and assess how results would change.

This chapter brings together the argument so far, showing how nonignorable nonresponse may manifest itself and how the various models perform across these contexts, including how they may fail. It also highlights the ideal response to potential nonignorable nonresponse, which involves (1) creating randomized instruments, (2) using the randomized instrument to diagnose nonignorable nonresponse, (3) moving to conventional weights if there is no evidence of nonignorable nonresponse, but (4) using selection models explained here when there is evidence of nonignorable nonresponse. Section 11.1 simulates and analyzes data across a range of scenarios using multiple methods. Section 11.2 discusses how to diagnose whether nonresponse is nonignorable. Section 11.3 integrates the approaches with a decision tree based on properties of the data. Section 11.4 discusses how selection models can fail.

This chapter describes contemporary practices of probabilistic and nonprobabilistic pollsters. First, even pollsters who aspire to random sampling are doing something quite foreign to the random sampling paradigm. Continuing to use the language of random sampling is therefore becoming increasingly untenable. Second, the energy and growth in polling is concentrated in nonprobabilistic polls that do not even pretend to adhere to the tenets of the random sampling paradigm. When we use, teach, and critique such polls, we need a new language for assessing them. Finally, one of the biggest vulnerabilities for both probabilistic and nonprobabilistic polling is nonignorable nonresponse, something largely ignored in the current state of the art. It is striking that despite the incredible diversity of techniques currently deployed, academic and commercial pollsters mostly continue to use models that assume away nonignorable nonresponse.