Analyzing Spatial Models of Choice and Judgment with R

David A. Armstrong, II, Ryan Bakker, Royce Carroll, Christopher Hare, Keith T. Poole, Howard Rosenthal

Hardback
$55.96

eBook
from $31.00

February 7, 2014 by Chapman and Hall/CRC
Reference - 356 Pages - 81 B/W Illustrations
ISBN 9781466517158 - CAT# K15094
Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

FREE Standard Shipping!

was $69.95

$55.96

SAVE $13.99

Add to Cart
Add to Wish List

Features

  • Describes every step of how to build spatial models in the R environment
  • Presents methods for analyzing public opinion data and legislative roll call data
  • Illustrates how to graphically display the estimated models
  • Assumes basic familiarity with R and matrix algebra
  • Includes exercises at the end of most chapters
  • Provides R code and datasets used in the text on the book’s website

Summary

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.