Monte Carlo Simulation and Resampling Methods for Social Science
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A01=Jeffrey J. Harden
A01=Thomas M. Carsey
Applied Research
Author_Jeffrey J. Harden
Author_Thomas M. Carsey
Category=GPS
Category=JHB
Data Analysis
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Introduction to R
Monte Carlo Simulation
Political Science Statistics
Quantitative Methods
Quantitative Research
Random Number Generation
Resampling Methods
Simulating Generalized Linear Models
Statistical Computing Environments
Statistical Research
Statistical Simulation of the Linear Model
Product details
- ISBN 9781452288901
- Weight: 560g
- Dimensions: 187 x 231mm
- Publication Date: 17 Sep 2013
- Publisher: SAGE Publications Inc
- Publication City/Country: US
- Product Form: Paperback
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Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, this book examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.
Thomas M. Carsey was the Thomas J. Pearsall Distinguished Professor of Political Science and Director of the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill. His research interests revolved around representation in American politics and quantitative methods. Within American politics, Carsey’s work focused on state politics, campaigns and elections, public opinion and mass behavior, partisanship and party polarization, and legislative politics. His methodological interests included all aspects of computational social science with specific interests in Monte Carlo simulation, resampling methods, clustered and pooled data, and methods for contextual analysis. Carsey’s research was funded by several grants from the National Science Foundation, and he published articles in journals such as American Political Science Review, American Journal of Political Science, Journal of Politics, State Politics & Policy Quarterly, and many others. Jeffrey J. Harden is an assistant professor in the Department of Political Science at the University of Colorado, Boulder specializing in political methodology and American politics. He received his PhD in political science from the University of North Carolina at Chapel Hill. His methodology interests include model selection, robust regression methods, multilevel data, and the use of Monte Carlo simulation to better understand issues that arise in applied analysis. His research agenda in American politics focuses on political representation, mass/elite linkages, and state politics. Harden has published articles in Political Analysis, Sociological Methods & Research, Legislative Studies Quarterly, State Politics & Policy Quarterly, and Public Choice.
