Analyzing Spatial Models of Choice and Judgment with R
Crc Press Inc (Verlag)
978-1-4665-1715-8 (ISBN)
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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.
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.
| Reihe/Serie | Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences |
|---|---|
| Zusatzinfo | 13 Tables, black and white; 81 Illustrations, black and white |
| Verlagsort | Bosa Roca |
| Sprache | englisch |
| Maße | 156 x 235 mm |
| Gewicht | 680 g |
| Themenwelt | Geisteswissenschaften ► Psychologie |
| Mathematik / Informatik ► Mathematik | |
| Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung | |
| Wirtschaft ► Volkswirtschaftslehre | |
| ISBN-10 | 1-4665-1715-8 / 1466517158 |
| ISBN-13 | 978-1-4665-1715-8 / 9781466517158 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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