Overview

Elections are random events. From individuals deciding whether to vote, to people deciding for whom to vote, to election authorities deciding what to count, the outcomes of competitive democratic elections are rarely known until election day…or beyond. Understanding Elections through Statistics: Polling, Prediction, and Testing explores this random phenomenon from two points of view: predicting the election outcome using opinion polls and testing the election outcome using government-reported data. Written for those with only a brief introduction to statistics, this book takes you on a statistical journey from how polls are taken to how they can—and should—be used to estimate current popular opinion. Once an understanding of the election process is built, we turn toward testing elections for evidence of unfairness. While holding elections has become the de facto proof of government legitimacy, those electoral processes may hide a dirty little secret of the government illicitly ensuring a favorable election outcome. This book includes these features designed to make your statistical journey more enjoyable: Vignettes of elections, including maps, to provide concrete bases for the material In-chapter cues to help one avoid the heavy math—or to focus on it End-of-chapter problems designed to review and extend that which was covered in the chapter Many opportunities to turn the power of the R statistical environment to the enclosed election data files, as well as to those you find interesting From these features, it is clear the audience for this book is quite diverse. This text provides mathematics for those interested in mathematics, but also offers detours for those who just want a good read and a deeper understanding of elections. Author Ole J. Forsberg holds PhDs in both political science and statistics. He currently teaches mathematics and statistics in the Department of Mathematics at Knox College in Galesburg, IL.

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Reviews

This unique book, by an author who is both a Statistician and Political Scientist, discusses the statistical theory of two important aspects of elections. The first half is an in-depth introduction to the classical statistical theory of polling, including estimators, confidence intervals, and stratified sampling. It comes complete with snippets of R code and many concrete examples, including two cases that challenged pollsters: the 2016 US presidential election and the 2016 Brexit vote. The second half concerns statistical methods for after the fact detection of fraudulent elections. It includes an in-depth treatment of methods based on the Benford distribution, but also methods based on classical regression analysis. Again numerous pieces of R code and concrete examples are provided. - E. Arthur Robinson, Jr., Professor of Mathematics, George Washington University This book has multiple layers that provides flexibility in its use. It makes polling and the statistical issues understandable for those who have little knowledge of statistics beyond the elementary course material. It includes enough of the mathematical underpinnings so that a student wishing to delve deeper into the material has that opportunity. It treats the material with cleverness and wryness that transforms the topic, usually thought of as dry by many people, into an interesting and compelling read. The use of maps and real-world examples help make the issues relevant and practical. It should be required reading for anyone studying political science and polling/elections, or anyone with a methodological background wishing to understand these topics at a greater depth. - Mark Payton, Rocky Vista University The book contains a list of 145 most recent references and a detailed index. Many exercises and appendices with mathematical derivations are given at the end of chapters. Numerous R scripts are presented throughout the whole monograph, providing ready-to-run or make-it-yourself tools for practical implementation of all the techniques. The book can be interesting not only to students in political sciences and statistical methods but to a wider audience interested to understand the results and checkup fairness of elections. - Stan Lipovetsky, Technometrics January 2021

This unique book, by an author who is both a Statistician and Political Scientist, discusses the statistical theory of two important aspects of elections. The first half is an in-depth introduction to the classical statistical theory of polling, including estimators, confidence intervals, and stratified sampling. It comes complete with snippets of R code and many concrete examples, including two cases that challenged pollsters: the 2016 US presidential election and the 2016 Brexit vote. The second half concerns statistical methods for after the fact detection of fraudulent elections. It includes an in-depth treatment of methods based on the Benford distribution, but also methods based on classical regression analysis. Again numerous pieces of R code and concrete examples are provided. - E. Arthur Robinson, Jr., Professor of Mathematics, George Washington University

Author Information

Ole J. Forsberg, BS, MAT, MA, MSE, PhDd, is an Assistant Professor of Mathematics-Statistics at Knox College in Galesburg, IL. He received a PhD in Political Science at the University of Tennessee–Knoxville in 2006, concentrating in International Relations, War, and Terrorism. After finishing his dissertation, Dr Forsberg began a deeper investigation of the statistical techniques he used. As a result of that embarrassment, Dr Forsberg began statistical studies at the Johns Hopkins University (MSE, 2010) and concluded them with a PhD in Statistics from Oklahoma State University in 2014. His dissertation explored and applied applications of statistical techniques to testing elections for violations of the “free and fair” democratic claim. His research agenda lies in extending and applying statistical methods to modeling elections and testing the results for evidence of bias in election results.

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