Monte Carlo Simulation and Resampling Methods for Social Science

Name: Monte Carlo Simulation and Resampling Methods for Social Science
Availability: InStock
ISBN: 9781452288901

ISBN-10: 1452288909

ISBN-13: 9781452288901

Edition: 2014

Authors: Thomas M. Carsey, Jeffrey J. Harden

List price: $95.00

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Description:

Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, this book illustrates 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 students 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…

Book details

List price: $95.00
Copyright year: 2014
Publisher: SAGE Publications, Incorporated
Publication date: 8/6/2013
Binding: Paperback
Pages: 304
Size: 7.38" wide x 9.13" long x 0.63" tall
Weight: 1.342

Jeffrey J. Harden is an assistant professor in the Department of Political Science at the University of Colorado Boulder. He received a PhD in Political Science at the University of North Carolina, Chapel Hill in 2012. His research focuses on political representation, American state politics, and quantitative methodology. He has published articles in the American Political Science Review, Political Analysis, Legislative Studies Quarterly, and State Politics and Policy Quarterly. This book is based on his dissertation, which was a co-recipient of the 2013 Christopher Z. Mooney Award from the American Political Science Association's State Politics and Policy Section.



Acknowledgments



Introduction



Can You Repeat That Please?



Simulation and Resampling Methods



Simulations as Experiments



Simulations Help Develop Intuition



An Overview of Simulation



Resampling Methods as Simulation



OLS as a Motivating Example



Two Brief Examples



Example 1: A Statistical Simulation



Example 2: A Substantive Theory Simulation



Looking Ahead



Assumed Knowledge



A Preview of the Book



R Packages



Probability



Introduction



Some Basic Rules of Probability



Introduction to Set Theory



Properties of Probability



Conditional Probability



Simple Math With Probabilities



Random Variables and Probability Distributions



Discrete Random Variables



Some Common Discrete Distributions



Continuous Random Variables



Two Common Continuous Distributions



Other Continuous Distributions



Conclusions



Introduction to R



Introduction



What Is R?



Resources



Using R With a Text Editor



First Steps



Creating Objects



Basic Manipulation of Objects



Vectors and Sequences



Matrices



Functions



Matrix Algebra Functions



Creating New Functions



Working With Data



Loading Data



Exploring the Data



Statistical Models



Generalized Linear Models



Basic Graphics



Conclusions



Random Number Generation



Introduction



Probability Distributions



Drawing Random Numbers



Creating Your Own Distribution Functions



Systematic and Stochastic



The Systematic Component



The Stochastic Component



Repeating the Process



Programming in R



for Loops



Efficient Programming



If-Else



Completing the OLS Simulation



Anatomy of a Script File



Statistical Simulation of the Linear Model



Introduction



Evaluating Statistical Estimators



Bias, Efficiency, and Consistency



Measuring Estimator Performance in R



Simulations as Experiments



Heteroskedasticity



Multicollinearity



Measurement Error



Omitted Variable



Serial Correlation



Clustered Data



Heavy-Tailed Errors



Conclusions



Simulating Generalized Linear Models



Introduction



Simulating OLS as a Probability Model



Simulating GLMs



Binary Models



Ordered Models



Multinomial Models



Extended Examples



Ordered or Multinomial?



Count Models



Duration Models



Computational Issues for Simulations



Research Computing



Parallel Processing



Conclusions



Testing Theory Using Simulation



Introduction



What Is a Theory?



Zipf's Law



Testing Zipf's Law With Frankenstein



From Patterns to Explanations



Punctuated Equilibrium and Policy Responsiveness



Testing Punctuated Equilibrium Theory



From Patterns to Explanations



Dynamic Learning



Reward and Punishment



Damned If You Do, Damned If You Don't



The Midas Touch



Conclusions



Resampling Methods



Introduction



Permutation and Randomization Tests



A Basic Permutation Test



Randomization Tests



Permutation/Randomization and Multiple Regression Models



Jackknifing



An Example



An Application: Simulating Heteroskedasticity



Pros and Cons of Jackknifing



Bootstrapping



Bootstrapping Basics



Bootstrapping With Multiple Regression Models



Adding Complexity: Clustered Bootstrapping



Conclusions



Other Simulation-Based Methods



Introduction



QI Simulation



Statistical Overview



Examples



Simulating QI With Zelig



Average Case Versus Observed Values



The Benefits of QI Simulation



Cross-Validation



How CV Can Help



An Example



Using R Functions for CV



Conclusions



Final Thoughts



A Summary of the Book



Going Forward



Conclusions


References


Index