Ole J. Forsberg

eBook, ePUB

Understanding Elections through Statistics (eBook, ePUB)

Polling, Prediction, and Testing

Beschreibung

Elections are random events.

From individuals deciding whether to vote, to individuals deciding who to vote for, 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 explores this random phenomenon from three primary points of view: predicting the election outcome using opinion polls, testing the election outcome using government-reported data, and exploring election data to better understand the people.

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 the 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, starting each chapter to motivate 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 what 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

The second edition improves upon this and includes:

• A rewrite of several chapters to make the underlying concepts more clear
• A chapter dedicated to confidence intervals, what they mean, and what they do not
• Additional experiments to help you better understand the statistics of elections
• A new introduction to polling, its terms, its processes, and its ethics

From these features, it is clear that the audience for this book is quite diverse. It provides the statistics and mathematics for those interested in statistics and mathematics, but it also provides detours for those who just want a good read and a deeper understanding of elections.

Technische Details

• Geräte: eReader
• mit Kopierschutz

Barrierefreiheit

• Text-to-Speech
• E-Mail des Verlags für Barrierefreiheitsfragen: [e-mail]
• Mathematische Inhalte in MathML für assistive Technologien
• Kurze Alternativtexte für nicht-textuelle Inhalte vorhanden

Autor

Ole J. Forsberg, PhD, is an Associate Professor of Mathematics-Statistics and the Associate Dean of Faculty Affairs at Knox College in Galesburg, IL. He received a PhD in Political Science from the University of Tennessee-Knoxville in 2006, concentrating in International Relations, War, and Terrorism. After finishing his dissertation, Forsberg began a deeper investigation of the statistical techniques he used there. As a result of the ensuing embarrassment, he began formal graduate statistical studies at the Johns Hopkins University (MSE, 2010) and concluded them with a PhD in Statistics from Oklahoma State University in 2014 (Go Pokes!). His statistics dissertation explored and applied 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 elections with the goal of drawing as much out of the available information as possible. . . whether this is in terms of understanding political polls, predicting election outcomes, or testing elections for unfairness.

Produktdetails

• Verlag: Taylor & Francis eBooks
• Erscheinungstermin: 31. Oktober 2024
• Englisch
• ISBN-13: 9781040130681
• Artikelnr.: 72292843

Rezensionen

"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