Buch, Englisch, 356 Seiten, Format (B × H): 167 mm x 245 mm, Gewicht: 682 g
Reihe: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Buch, Englisch, 356 Seiten, Format (B × H): 167 mm x 245 mm, Gewicht: 682 g
Reihe: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
ISBN: 978-1-4665-1715-8
Verlag: Taylor & Francis Inc
Modern Methods for Evaluating Your Social Science Data
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—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.
Zielgruppe
Researchers and graduate students in the social sciences, including those in educational psychology and political science.
Autoren/Hrsg.
Weitere Infos & Material
Introduction The Spatial Theory of VotingSummary of Data Types Analyzed by Spatial Voting Models
The Basics Data Basics in RReading Data in R Writing Data in R
Analyzing Issue Scales Aldrich-McKelvey ScalingBasic Space Scaling: The blackbox FunctionBasic Space Scaling: The blackbox transpose FunctionAnchoring 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 PackageParametric Methods—NOMINATEMCMC or a-NOMINATE Parametric Methods—Bayesian Item Response TheoryNonparametric Methods—Optimal Classification
Advanced TopicsUsing Latent Estimates as Variables Ordinal and Dynamic IRT Models
Conclusion and Exercises appear at the end of each chapter.
