Analyzing Spatial Models of Choice and Judgment with R

Chapman & Hall/CRC the R
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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.
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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.

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Autor: David A. Armstrong II
ISBN-13 :: 9781466517158
ISBN: 1466517158
Erscheinungsjahr: 07.02.2014
Verlag: CRC PR INC
Gewicht: 682g
Seiten: 356
Sprache: Englisch
Auflage New
Sonstiges: Buch, 245x167x27 mm
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