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E-raamat: Monte Carlo Simulation and Resampling Methods for Social Science

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 05-Aug-2013
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781483313474
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Monte Carlo Simulation and Resampling Methods for Social ScienceSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 05-Aug-2013
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781483313474
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Carsey (U. of North Carolina-Chapel Hill) and Harden (U. of Colorado-Boulder) introduce students and researchers in the social sciences to a set of quantitative methods that could help them draw inferences about the larger population from which the data was drawn. The information would be most useful to scholars who have some training in quantitative methods. Among the topics are probability, random number generation, the statistical simulation of the linear model, testing theory using simulation, and other simulation-based methods. Annotation ©2013 Book News, Inc., Portland, OR (booknews.com)

Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, Monte Carlo Simulation and Resampling Methods for Social Science, by Thomas M. Carsey and Jeffrey J. Harden, 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.

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 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.

Arvustused

There is no text like this that is geared toward a social science market.

                                            -- Wendy K. Tam Cho [ The] writing is direct and to the point... I cant underemphasize that part. Too many methods books try to soften the technical edge by throwing in lots of commentary. -- Paul Johnson Bradley Efron discussed the newly-invented bootstrap and other computationally intensive statistical techniques in a 1979 article entitled "Computers and the Theory of Statistics: Thinking the Unthinkable." But as computer power grew exponentially and software for simulation greatly improved, what was once unthinkable has become routine. Carsey and Harden have performed a service by making modern tools for random simulation and resampling methods (like the bootstrap) accessible to a broad readership in the social sciences, developing these methods from first principles, and showing how they can be applied both to understand statistical ideas and in practical data analysis. -- John Fox Statistical simulation has become an essential tool of modern statistics and data analysisuseful for evaluating estimators, calculating features of probability distributions, transforming difficult-to-interpret statistical results into meaningful quantities of interest, and even helping with alternative theories of inference. Simulation perspectives also offer a terrific way to learn many aspects of statistical modeling. Join Tom Carsey and Jeff Harden for a clearly written and deeply practical book on this crucial topic. Your scholarly work will be better for it. -- Gary King Carsey and Harden have written an intuitive and practical primer to a radicalbut increasingly widely usedapproach to statistical inference: Monte Carlo and resampling. They focus on using these techniques to evaluate more standard statistical approaches, but in the process, they convey their broader use and importance. They also teach the reader about statistical inference at a much more basic level than do most social science treatments of empirical methods. Their book is destined to be used widely in graduate social science statistics classes around the world. -- Christopher Mooney Monte Carlo simulation and resampling are the workhorse of modern methods. Carsey and Harden provide the perfect, accessible guide to learn this  fundamental, must-have skill for social scientists. -- Janet M. Box-Steffensmeier

Acknowledgments ix
1 Introduction 1(18)
1.1 Can You Repeat That Please?
2(2)
1.2 Simulation and Resampling Methods
4(4)
1.2.1 Simulations as Experiments
4(1)
1.2.2 Simulations Help Develop Intuition
5(1)
1.2.3 An Overview of Simulation
6(1)
1.2.4 Resampling Methods as Simulation
7(1)
1.3 OLS as a Motivating Example
8(4)
1.4 Two Brief Examples
12(3)
1.4.1 Example 1: A Statistical Simulation
13(2)
1.4.2 Example 2: A Substantive Theory Simulation
15(1)
1.5 Looking Ahead
15(2)
1.5.1 Assumed Knowledge
16(1)
1.5.2 A Preview of the Book
16(1)
1.6 R Packages
17(2)
2 Probability 19(26)
2.1 Introduction
19(1)
2.2 Some Basic Rules of Probability
20(4)
2.2.1 Introduction to Set Theory
20(2)
2.2.2 Properties of Probability
22(1)
2.2.3 Conditional Probability
22(1)
2.2.4 Simple Math With Probabilities
23(1)
2.3 Random Variables and Probability Distributions
24(5)
2.4 Discrete Random Variables
29(4)
2.4.1 Some Common Discrete Distributions
30(3)
2.5 Continuous Random Variables
33(10)
2.5.1 Two Common Continuous Distributions
36(3)
2.5.2 Other Continuous Distributions
39(4)
2.6 Conclusions
43(2)
3 Introduction to R 45(18)
3.1 Introduction
45(1)
3.2 What Is R?
45(1)
3.2.1 Resources
46(1)
3.3 Using R With a Text Editor
46(1)
3.4 First Steps
47(1)
3.4.1 Creating Objects
47(1)
3.5 Basic Manipulation of Objects
48(2)
3.5.1 Vectors and Sequences
48(1)
3.5.2 Matrices
49(1)
3.6 Functions
50(2)
3.6.1 Matrix Algebra Functions
51(1)
3.6.2 Creating New Functions
51(1)
3.7 Working With Data
52(7)
3.7.1 Loading Data
52(1)
3.7.2 Exploring the Data
53(1)
3.7.3 Statistical Models
54(3)
3.7.4 Generalized Linear Models
57(2)
3.8 Basic Graphics
59(2)
3.9 Conclusions
61(2)
4 Random Number Generation 63(20)
4.1 Introduction
63(1)
4.2 Probability Distributions
63(5)
4.2.1 Drawing Random Numbers
65(2)
4.2.2 Creating Your Own Distribution Functions
67(1)
4.3 Systematic and Stochastic
68(4)
4.3.1 The Systematic Component
69(1)
4.3.2 The Stochastic Component
70(1)
4.3.3 Repeating the Process
71(1)
4.4 Programming in R
72(5)
4.4.1 for Loops
73(1)
4.4.2 Efficient Programming
74(2)
4.4.3 If-Else
76(1)
4.5 Completing the OLS Simulation
77(6)
4.5.1 Anatomy of a Script File
80(3)
5 Statistical Simulation of the Linear Model 83(44)
5.1 Introduction
83(1)
5.2 Evaluating Statistical Estimators
84(12)
5.2.1 Bias, Efficiency, and Consistency
84(3)
5.2.2 Measuring Estimator Performance in R
87(9)
5.3 Simulations as Experiments
96(29)
5.3.1 Heteroskedasticity
96(7)
5.3.2 Multicollinearity
103(2)
5.3.3 Measurement Error
105(4)
5.3.4 Omitted Variable
109(3)
5.3.5 Serial Correlation
112(2)
5.3.6 Clustered Data
114(4)
5.3.7 Heavy-Tailed Errors
118(7)
5.4 Conclusions
125(2)
6 Simulating Generalized Linear Models 127(42)
6.1 Introduction
127(1)
6.2 Simulating OLS as a Probability Model
128(2)
6.3 Simulating GLMs
130(15)
6.3.1 Binary Models
130(5)
6.3.2 Ordered Models
135(6)
6.3.3 Multinomial Models
141(4)
6.4 Extended Examples
145(17)
6.4.1 Ordered or Multinomial?
145(5)
6.4.2 Count Models
150(7)
6.4.3 Duration Models
157(5)
6.5 Computational Issues for Simulations
162(5)
6.5.1 Research Computing
162(1)
6.5.2 Parallel Processing
163(4)
6.6 Conclusions
167(2)
7 Testing Theory Using Simulation 169(32)
7.1 Introduction
169(1)
7.2 What Is a Theory?
169(2)
7.3 Zipf's Law
171(10)
7.3.1 Testing Zipf's Law With Frankenstein
171(3)
7.3.2 From Patterns to Explanations
174(7)
7.4 Punctuated Equilibrium and Policy Responsiveness
181(9)
7.4.1 Testing Punctuated Equilibrium Theory
183(2)
7.4.2 From Patterns to Explanations
185(5)
7.5 Dynamic Learning
190(10)
7.5.1 Reward and Punishment
193(2)
7.5.2 Damned If You Do, Damned If You Don't
195(2)
7.5.3 The Midas Touch
197(3)
7.6 Conclusions
200(1)
8 Resampling Methods 201(30)
8.1 Introduction
201(1)
8.2 Permutation and Randomization Tests
202(7)
8.2.1 A Basic Permutation Test
203(2)
8.2.2 Randomization Tests
205(3)
8.2.3 Permutation/Randomization and Multiple Regression Models
208(1)
8.3 Jackknifing
209(6)
8.3.1 An Example
210(3)
8.3.2 An Application: Simulating Heteroskedasticity
213(1)
8.3.3 Pros and Cons of Jackknifing
214(1)
8.4 Bootstrapping
215(13)
8.4.1 Bootstrapping Basics
217(3)
8.4.2 Bootstrapping With Multiple Regression Models
220(5)
8.4.3 Adding Complexity: Clustered Bootstrapping
225(3)
8.5 Conclusions
228(3)
9 Other Simulation-Based Methods 231(38)
9.1 Introduction
231(1)
9.2 QI Simulation
232(23)
9.2.1 Statistical Overview
232(3)
9.2.2 Examples
235(10)
9.2.3 Simulating QI With zelig
245(4)
9.2.4 Average Case Versus Observed Values
249(5)
9.2.5 The Benefits of QI Simulation
254(1)
9.3 Cross-Validation
255(12)
9.3.1 How CV Can Help
257(1)
9.3.2 An Example
258(8)
9.3.3 Using R Functions for CV
266(1)
9.4 Conclusions
267(2)
10 Final Thoughts 269(6)
10.1 A Summary of the Book
270(1)
10.2 Going Forward
271(1)
10.3 Conclusions
272(3)
References 275(8)
Index 283
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, Carseys 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. Carseys 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.
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