Record Display for the EPA National Library Catalog


Main Title Analyzing spatial models of choice and judgment with R /
Author Armstrong, David A.,
Publisher CRC Press, Taylor & Francis Group,
Year Published 2014
OCLC Number 773024885
ISBN 9781466517158; 1466517158
Subjects Spatial analysis (Statistics) ; Spatial behavior--Mathematical models ; Spatial behavior--Political aspects ; Legislative bodies--Voting--Data processing ; R (Computer program language) ; Wahlforschung ; Räumliche Statistik
Library Call Number Additional Info Location Last
EJAM  QA278.2.A76 2014 Region 3 Library/Philadelphia, PA 09/16/2016 DISPERSAL
EKBM  QA278.2.A76 2014 Research Triangle Park Library/RTP, NC 10/04/2022
ELBM  QA278.2.A76 2014 AWBERC Library/Cincinnati,OH 10/19/2015
Collation xx, 336 pages.
Includes bibliographical references (pages 311-329) and index.
Contents Notes
1. Introduction -- 2. The basics -- 3. Analyzing issue scales -- 4. Analyzing similarities and dissimilarities data -- 5. Unfolding analysis of rating scale data -- 6. Unfolding analysis of binary choice data -- 7. Advanced topics. With recent advances in computing power and the widespread availability of political choice data, such as legislative roll call and public opinion survey data, the empirical estimation of spatial models has never been easier or more popular. Analyzing Spatial Models of Choice and Judgment with R demonstrates how to estimate and interpret spatial models using a variety of methods with the popular, open-source programming language R. Requiring basic knowledge of R, the book enables researchers to apply the methods to their own data. Also suitable for expert methodologists, it presents the latest methods for modeling the distances between points �not the locations of the points themselves. This distinction has important implications for understanding scaling results, particularly how uncertainty spreads throughout the entire point configuration and how results are identified. In each chapter, the authors explain the basic theory behind the spatial model, then illustrate the estimation techniques and explore their historical development, and finally discuss the advantages and limitations of the methods. They also demonstrate step by step how to implement each method using R with actual datasets. The R code and datasets are available on the book's website.--