Synopses & Reviews
Review
R is an interpreted language designed for statistical computation.Free, ubiquitous, and powerful, it is an excellent choice for such computations and is rapidly becoming a de facto standard in thefield. This text concentrates on construction and analysis of spatial models using R, particularly in the fields of politicalchoice data. Introductory chapters describe both the political context (the spatial theory of voting and data types analyzed byspatial voting models) and provide a foundation for programming in R (although this text does not replace a basic introduction to Rprogramming). Further chapters address specific issues on the models, such as scales, similarities and dissimilarities data, ratingand binary choice data, and several advanced topics in latent estimates and ordinal and dynamic IRT models. Examples ofcalculations using international data on politics and voting patterns, and many fully-worked programming examples round out the text.Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
Synopsis
Modern Methods for Evaluating Your Social Science Data
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."