Survey research is in a state of crisis. People have become less willing to respond to polls and recent misses in critical elections have undermined the field's credibility. Pollsters have developed many tools for dealing with the new environment, an increasing number of which rely on risky opt-in samples. Virtually all of these tools require that respondents in each demographic category are a representative sample of all people in each demographic category, something that is unlikely to be reliably true. Polling at a Crossroads moves beyond such strong limitations, providing tools that work even when survey respondents are unrepresentative in complex ways. This book provides case studies that show how to avoid underestimating Trump support and how conventional polls exaggerate partisan differences. This book also helps us think in clear and sometimes counterintuitive ways and points toward simple, low-cost changes that can better address contemporary polling challenges.

‘Throughout the masterful Polling at a Crossroads, Bailey provides a lucid explanation of the problems pollsters face and a compelling argument for why a new approach is needed. By bringing ideas and insights from various disciplines together, Bailey shows how pollsters can move beyond past approaches to tackle increasingly consequential issues related to survey non-response; respondents now differ from non-respondents in unobservable ways. In describing the issues confronting contemporary polling, the book builds to a convincing and forceful advocacy for the use of selection models in survey research and, in so doing, it provides a perspective that every pollster must consider going forward.’

Josh Clinton - Abby and Jon Winkelried Chair, Vanderbilt University

‘The idea of learning about a country of 300 millions from a sample of 300 is extremely seductive. But seduction often comes with dangers. Bailey's timely book explains why the dangers are increasingly fatal, and how the field of polling may save itself.’

Xiao-Li Meng - Whipple V. N. Jones Professor of Statistics, Harvard University