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The Resource Analyzing spatial models of choice and judgment with R, David A. Armstrong II, University of Wisconsin-Milwaukee, Ryan Bakker, University of Georgia, Royce Carroll, Rice University, Christopher Hare, University of Georgia, Keith T. Poole, Howard Rosenthal, New York University

Analyzing spatial models of choice and judgment with R, David A. Armstrong II, University of Wisconsin-Milwaukee, Ryan Bakker, University of Georgia, Royce Carroll, Rice University, Christopher Hare, University of Georgia, Keith T. Poole, Howard Rosenthal, New York University

Label
Analyzing spatial models of choice and judgment with R
Title
Analyzing spatial models of choice and judgment with R
Statement of responsibility
David A. Armstrong II, University of Wisconsin-Milwaukee, Ryan Bakker, University of Georgia, Royce Carroll, Rice University, Christopher Hare, University of Georgia, Keith T. Poole, Howard Rosenthal, New York University
Creator
Subject
Language
eng
Summary
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<U+0127> �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.--
Member of
Assigning source
Provided by Publisher
Cataloging source
DLC
http://library.link/vocab/creatorDate
1976-
http://library.link/vocab/creatorName
Armstrong, David A.
Dewey number
300.72
Index
index present
Literary form
non fiction
Nature of contents
bibliography
Series statement
Statistics in the social and behavioral sciences series
http://library.link/vocab/subjectName
  • Spatial analysis (Statistics)
  • Spatial behavior
  • Spatial behavior
  • Legislative bodies
  • R (Computer program language)
Label
Analyzing spatial models of choice and judgment with R, David A. Armstrong II, University of Wisconsin-Milwaukee, Ryan Bakker, University of Georgia, Royce Carroll, Rice University, Christopher Hare, University of Georgia, Keith T. Poole, Howard Rosenthal, New York University
Instantiates
Publication
Bibliography note
Includes bibliographical references (pages 311-329) and index
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier.
Content category
text
Content type code
txt
Content type MARC source
rdacontent.
Contents
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<U+0127> �NOMINATE MCMC or a-NOMINATE Parametric Methods<U+0127> �Bayesian Item Response Theory Nonparametric Methods<U+0127> �Optimal Classification Advanced Topics Using Latent Estimates as Variables Ordinal and Dynamic IRT Models
Control code
FIEb17644653
Dimensions
cm.
Extent
xx, 336 pages
Isbn
9781466517158
Media category
unmediated
Media MARC source
rdamedia.
Media type code
n
System control number
(OCoLC)773024885
Label
Analyzing spatial models of choice and judgment with R, David A. Armstrong II, University of Wisconsin-Milwaukee, Ryan Bakker, University of Georgia, Royce Carroll, Rice University, Christopher Hare, University of Georgia, Keith T. Poole, Howard Rosenthal, New York University
Publication
Bibliography note
Includes bibliographical references (pages 311-329) and index
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier.
Content category
text
Content type code
txt
Content type MARC source
rdacontent.
Contents
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<U+0127> �NOMINATE MCMC or a-NOMINATE Parametric Methods<U+0127> �Bayesian Item Response Theory Nonparametric Methods<U+0127> �Optimal Classification Advanced Topics Using Latent Estimates as Variables Ordinal and Dynamic IRT Models
Control code
FIEb17644653
Dimensions
cm.
Extent
xx, 336 pages
Isbn
9781466517158
Media category
unmediated
Media MARC source
rdamedia.
Media type code
n
System control number
(OCoLC)773024885

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