| Description |
1 online resource (320 p.) |
| Series |
Chapman and Hall/CRC Statistics in the Social and Behavioral Sciences Ser |
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Chapman and Hall/CRC Statistics in the Social and Behavioral Sciences Ser
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| 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) |
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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) |
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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 |
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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) |
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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) |
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5.4 Nonparametric Methods -- Optimal Classification |
| Notes |
Description based upon print version of record |
| Form |
Electronic book
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| Author |
Bakker, Ryan
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Carroll, Royce
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Hare, Christopher
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Poole, Keith T
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Rosenthal, Howard
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| ISBN |
9781351770507 |
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1351770500 |
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