Analyzing spatial models of choice and judgment / David A. Armstrong II [and five others].

Format:

Book

Author/Creator:

Armstrong, David A., II, 1976- author.

Contributor:

Taylor & Francis eBooks.

Series:

Statistics in the social and behavioral sciences series

Chapman & Hall/CRC statistics in the social and behavioral sciences series

Language:

English

Subjects (All):

Spatial analysis (Statistics).

Spatial behavior--Mathematical models.

Spatial behavior.

Spatial behavior--Political aspects.

Legislative bodies--Voting--Data processing.

Legislative bodies.

R (Computer program language).

Legislative bodies--Voting.

Physical Description:

1 online resource (xviii, 301 pages) : illustrations.

polychrome

Edition:

Second edition.

Place of Publication:

Boca Raton : CRC Press, 2021.

System Details:

text file

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

Notes:

Includes bibliographical references and index.

Electronic reproduction. London Available via World Wide Web.

Print version record.

Other Format:

Print version: Armstrong, David A., II, 1976- Analyzing spatial models of choice and judgment.

ISBN:

9781351770491

1351770497

9781351770507

1351770500

9781315197609

131519760X

9781351770484

1351770489

Publisher Number:

99989360194

Access Restriction:

Restricted for use by site license.