Analyzing Spatial Models of Choice and Judgment (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

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

• Publisher: Chapman and Hall/CRC
• Year: 2020
• Tongue: English
• Leaves: 320
• Edition: 2
• Category: Library

✦ Synopsis

With recent advances in computing power and the widespread availability of preference, perception and choice data, such as public opinion surveys and legislative voting, the empirical estimation of spatial models using scaling and ideal point estimation methods has never been more accessible.The second edition of Analyzing Spatial Models of Choice and Judgment demonstrates how to estimate and interpret spatial models with a variety of methods using the open-source programming language R.

Requiring only basic knowledge of R, the book enables social science researchers to apply the methods to their own data. Also suitable for experienced methodologists, it presents the latest methods for modeling the distances between points.

The authors explain the basic theory behind empirical spatial models, then illustrate the estimation technique behind implementing each method, exploring the advantages and limitations while providing visualizations to understand the results.

This second edition updates and expands the methods and software discussed in the first edition, including new coverage of methods for ordinal data and anchoring vignettes in surveys, as well as an entire chapter dedicated to Bayesian methods. The second edition is made easier to use by the inclusion of an R package, which provides all data and functions used in the book.

David A. Armstrong II is Canada Research Chair in Political Methodology and Associate Professor of Political Science at Western University. His research interests include measurement, Democracy and state repressive action.

Ryan Bakker is Reader in Comparative Politics at the University of Essex. His research interests include applied Bayesian modeling, measurement, Western European politics, and EU politics.

Royce Carroll is Professor in Comparative Politics at the University of Essex. His research focuses on measurement of ideology and the comparative politics of legislatures and political parties.

Christopher Hare is Assistant Professor in Political Science at the University of California, Davis. His research focuses on ideology and voting behavior in US politics, political polarization, and measurement.

Keith T. Poole is Philip H. Alston Jr. Distinguished Professor of Political Science at the University of Georgia. His research interests include methodology, US political-economic history, economic growth and entrepreneurship.

Howard Rosenthal is Professor of Politics at NYU and Roger Williams Straus Professor of Social Sciences, Emeritus, at Princeton. Rosenthal’s research focuses on political economy, American politics and methodology.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Author Biographies
1 Introduction
1.1 The Spatial Theory of Voting
1.1.1 Theoretical Development and Applications of the Spatial Voting Model
1.1.2 The Development of Empirical Estimation Methods for Spatial Models of Voting
1.1.3 The Basic Space Theory
1.2 Summary of Data Types Analyzed by Spatial Voting Models
1.3 Conclusion
2 Analyzing Issue Scales
2.1 Aldrich-McKelvey Scaling
2.1.1 The basicspace Package in R
2.1.2 Example 1: 2009 European Election Study (French Module)
2.1.3 Example 2: 1968 American National Election Study Urban Unrest and Vietnam War Scales
2.1.4 Estimating Bootstrapped Standard Errors for Aldrich-McKelvey Scaling
2.2 Basic Space Scaling: The blackbox Function
2.2.1 Example 1: 2000 Convention Delegate Study
2.2.2 Example 2: 2010 Swedish Parliamentary Candidate Survey
2.2.3 Estimating Bootstrapped Standard Errors for Black Box Scaling
2.3 Basic Space Scaling: The blackbox_transpose Function
2.3.1 Example 1: 2000 and 2006 Comparative Study of Electoral Systems (Mexican Modules)
2.3.2 Estimating Bootstrapped Standard Errors for Black Box Transpose Scaling
2.3.3 Using the blackbox_transpose Function on Data sets with Large Numbers of Respondents
2.4 Ordered Optimal Classification
2.5 Using Anchoring Vignettes
2.6 Conclusion
2.7 Exercises
3 Analyzing Similarities and Dissimilarities Data
3.1 Classical Metric Multidimensional Scaling
3.1.1 Example 1: Nations Similarities Data
3.1.2 Metric MDS Using Numerical Optimization
3.1.3 Metric MDS Using Majorization (SMACOF)
3.1.4 The smacof Package in R
3.2 Nonmetric Multidimensional Scaling
3.2.1 Example 1: Nations Similarities Data
3.2.2 Example 2: 90th US Senate Agreement Scores
3.3 Individual Differences Multidimensional Scaling
3.3.1 Example 1: 2009 European Election Study (French Module)
3.4 Conclusion
3.5 Exercises
4 Unfolding Analysis of Rating Scale Data
4.1 Solving the Thermometers Problem
4.2 Metric Unfolding Using the MLSMU6 Procedure
4.2.1 Example 1: 1981 Interest Group Ratings of US Senators Data
4.3 Metric Unfolding Using Majorization (SMACOF)
4.3.1 Example 1: 2009 European Election Study (Danish Module)
4.3.2 Comparing the MLSMU6 and SMACOF Metric Unfolding Procedures
4.4 Conclusion
4.5 Exercises
5 Unfolding Analysis of Binary Choice Data
5.1 The Geometry of Legislative Voting
5.2 Reading Legislative Roll Call Data into R with the pscl Package
5.3 Parametric Methods - NOMINATE
5.3.1 Obtaining Uncertainty Estimates with the Parametric Bootstrap
5.3.2 Types of NOMINATE Scores
5.3.3 Accessing DW-NOMINATE Scores
5.3.4 The wnominate Package in R
5.3.5 Example 1: The 108th US House
5.3.6 Example 2: The First European Parliament (Using the Parametric Bootstrap)
5.4 Nonparametric Methods - Optimal Classification
5.4.1 The oc Package in R
5.4.2 Example 1: The French National Assembly during the Fourth Republic
5.4.3 Example 2: 2008 American National Election Study Feeling Thermometers Data
5.5 Conclusion: Comparing Methods for the Analysis of Legislative Roll Call Data
5.5.1 Identication of the Model Parameters
5.5.2 Comparing Ideal Point Estimates for the 111th US Senate
5.6 Exercises
6 Bayesian Scaling Models
6.1 Bayesian Aldrich-McKelvey Scaling
6.1.1 Comparing Aldrich-McKelvey Standard Errors
6.2 Bayesian Multidimensional Scaling
6.2.1 Example 1: Nations Similarities Data
6.3 Bayesian Multidimensional Unfolding
6.3.1 Example 2: 1968 American National Election Study Feeling Thermometers Data
6.4 Parametric Methods - Bayesian Item Response Theory
6.4.1 The MCMCpack and pscl Packages in R
6.4.2 Example 3: The 2000 Term of the US Supreme Court (Unidimensional IRT)
6.4.3 Running Multiple Markov Chains in MCMCpack and pscl
6.4.4 Example 4: The Confirmation Vote of Robert Bork to the US Supreme Court (Unidimensional IRT)
6.4.5 Example 5: The 89th US Senate (Multidimensional IRT)
6.4.6 Identification of the Model Parameters
6.5 MCMC or α-NOMINATE
6.5.1 The anominate Package in R
6.6 Ordinal and Dynamic IRT Models
6.6.1 IRT with Ordinal Choice Data
6.6.2 Dynamic IRT
6.7 EM IRT
6.8 Conclusion
6.9 Exercises
References
Index