Analyzing Spatial Models of Choice and Judgment with R

"The book is well organized. ... The R code in the book is well documented and the R outputs are clearly interpreted. ... The book is accessible to applied researchers who are more interested in applying the methods than in delving into their underlying theory. The step-by-step instructions given allow the reader to directly apply the methods. The understanding of the theoretical arguments, however, only requires college-level algebra." -Journal of the American Statistical Association, Vol. 110, 2015 "For someone working outside of the fields of spatial modeling and political science, simple and informative plots of results are vital to understanding exactly what spatial modeling is capable of in political science. This book emphasizes this need too, and the graphics provided help to answer questions on various issues from different countries. The book provides a user-friendly chapter on R and throughout offers simple summaries of established functions, such as optimization methods, which are valuable for any R user regardless of their research focus and ability. On top of these are useful descriptions and examples of more advanced packages for spatial modeling, with printed R code and exercises for the reader. This book appears to be a great tool for established political scientists and spatial modelers, as well as those new to the fields who want to get up to speed." -Significance, October 2014 "Analyzing Spatial Models of Choice and Judgment with R is the rare R-instructional book that succeeds on three levels. It clearly sets forth the psychological theory underlying its modeling method. It demonstrates how the mathematics used for the modeling provide principles of construction and interpretation consistent with that theory. And, it features very well-presented and sophisticated R code-sophisticated enough to bring novice users of R very far along the path of proficiency and even enough, in some sections, to educate and challenge more advanced users. Students and practitioners interested in this field, or in latent space modeling in general, should consider it essential reading." -Gary Evans, Journal of Statistical Software, June 2014

Introduction The Spatial Theory of Voting Summary of Data Types Analyzed by Spatial Voting Models The Basics Data Basics in R Reading Data in R Writing Data in R Analyzing Issue Scales Aldrich-McKelvey Scaling Basic Space Scaling: The blackbox Function Basic Space Scaling: The blackbox transpose Function Anchoring Vignettes Analyzing Similarities and Dissimilarities Data Classical Metric Multidimensional Scaling Non-Metric Multidimensional Scaling Bayesian Multidimensional Scaling Individual Differences Multidimensional Scaling Unfolding Analysis of Rating Scale Data Solving the Thermometers Problem Metric Unfolding Using the MLSMU6 Procedure Metric Unfolding Using Majorization (SMACOF) Bayesian Multidimensional Unfolding Unfolding Analysis of Binary Choice Data The Geometry of Legislative Voting Reading Legislative Roll Call Data into R with the pscl Package Parametric Methods-NOMINATE MCMC or a-NOMINATE Parametric Methods-Bayesian Item Response Theory Nonparametric Methods-Optimal Classification Advanced Topics Using Latent Estimates as Variables Ordinal and Dynamic IRT Models Conclusion and Exercises appear at the end of each chapter.