Analyzing spatial models of choice and judgment /

Bibliographic Details
Main Author:	Armstrong, David A., II, 1976- (Author)
Corporate Author:	EBSCOhost
Format:	eBook
Language:	English
Published:	Boca Raton : CRC Press, 2021.
Edition:	Second edition.
Series:	Statistics in the social and behavioral sciences series.
Subjects:	Spatial analysis (Statistics) Spatial behavior > Mathematical models. Spatial behavior > Political aspects. Legislative bodies > Voting > Data processing. R (Computer program language) Analyse spatiale (Statistique) Parlements > Vote > Informatique. R (Langage de programmation) spatial analysis. MATHEMATICS > Probability & Statistics > General. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Introduction
Analyzing issue scales
Analyzing similarities and dissimilarities data
Unfolding analysis of rating scale data
Unfolding analysis of binary choice data
Bayesian scaling models.
1. Introduction The Spatial Theory of Voting Theoretical Development and Applications of the Spatial Voting Model The Development of Empirical Estimation Methods for Spatial Models of Voting The Basic Space Theory Summary of Data Types Analyzed by Spatial Voting Models Conclusion 2. Analyzing Issue Scales Aldrich-McKelvey Scaling The basicspace Package in R Example : European Election Study (French Module) Example : American National Election Study Urban Unrest and Vietnam War Scales Estimating Bootstrapped Standard Errors for Aldrich- McKelvey Scaling Basic Space Scaling: The blackbox Function Example : Convention Delegate Study Example : Swedish Parliamentary Candidate Survey Estimating Bootstrapped Standard Errors for Black Box Scaling Basic Space Scaling: The blackbox transpose Function Example : and Comparative Study of Electoral Systems (Mexican Modules) Estimating Bootstrapped Standard Errors for Black Box Transpose Scaling Using the blackbox transpose Function on DatasetsOrdered Optimal Classi-cation Using Anchoring Vignettes Conclusion Exercises 3. Analyzing Similarities and Dissimilarities Data Classical Metric Multidimensional Scaling Example : Nations Similarities Data Metric MDS Using Numerical Optimization Metric MDS Using Majorization (SMACOF) The smacof Package in R Non-Metric Multidimensional Scaling Example : Nations Similarities Data Example : th US Senate Agreement Scores Individual Di-erences Multidimensional Scaling Example : European Election Study (French Module) Conclusion Exercises 4. Unfolding Analysis of Rating Scale Data Solving the Thermometers Problem Metric Unfolding Using the MLSMU Procedure Example : Interest Group Ratings of US Senators Data Metric Unfolding Using Majorization (SMACOF) Example : European Election Study (Danish Module) Comparing the MLSMU and SMACOF Metric Unfolding Procedures Conclusion Exercises 5. Unfolding Analysis of Binary Choice Data The Geometry of Legislative Voting Reading Legislative Roll Call Data into R with the pscl PackageParametric Methods
NOMINATE Obtaining Uncertainty Estimates with the Parametric Bootstrap Types of NOMINATE Scores Accessing DW-NOMINATE Scores The wnominate Package in R Example : The th US House Example : The First European Parliament (Using the Parametric Bootstrap) Nonparametric Methods
Optimal Classi-cation The oc Package in R Example : The French National Assembly during the Fourth Republic Example : American National Election Study Feeling Thermometers Data Conclusion: Comparing Methods for the Analysis of Legislative Roll Call Data Identi-cation of the Model Parameters Comparing Ideal Point Estimates for the th US Senate Exercises 6. Bayesian Scaling Models Bayesian Aldrich-McKelvey Scaling Comparing Aldrich-McKelvey Standard Errors Bayesian Multidimensional Scaling Example : Nations Similarities Data Bayesian Multidimensional Unfolding Example : American National Election Study Feeling Thermometers Data Parametric Methods
Bayesian Item Response Theory The MCMCpack and pscl Packages in R Example : The Term of the US Supreme Court (Unidimensional IRT) Running Multiple Markov Chains in MCMCpack and pscl Example : The Con-rmation Vote of Robert Bork to the US Supreme Court (Unidimensional IRT) Example : The th US Senate (Multidimensional IRT) Identi-cation of the Model Parameters MCMC or a-NOMINATE The anominate Package in R Ordinal and Dynamic IRT Models IRT with Ordinal Choice Data Dynamic IRT EM IRT Conclusion Exercises