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Monte Carlo Simulation and Resampling Methods for Social Science
First Edition
- Thomas M. Carsey - University of North Carolina, Chapel Hill, USA
- Jeffrey J. Harden - University of Notre Dame, US
August 2013 | 304 pages | SAGE Publications, Inc
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.
1. Introduction
2. Probability
3. Introduction to R
4. Random Number Generation
5 .Statistical Simulation of the Linear Model
6. Simulating Generalized Linear Models
7. Testing Theory Using Simulation
8. Resampling Methods
9. Other Simulation-Based Methods
10. Final Thoughts
This book is an excellent introduction to resampling and Monte Carlo methods in R. I highly recommend it.
Political Science, Brigham Young Univ-Idaho
December 13, 2014
This book is the very starting point to those graduate students who want to have a working understanding of simulations.
Retrovirus Research Center, Universidad Central del Caribe
May 5, 2014
