Monte Carlo Simulation

The author explains the logic behind the method and demonstrates its uses for social and behavioral research in: conducting inference using statistics with only weak mathematical theory; testing null hypotheses under a variety of plausible conditions; assessing the robustness of parametric inference to violations of its assumptions; assessing the quality of inferential methods; and comparing the properties of two or more estimators. In addition, Christopher Z Mooney carefully demonstrates how to prepare computer algorithms using GAUSS code and uses several research examples to demonstrate these principles.This volume will enable researchers to execute Monte Carlo Simulation effectively and to interpret the estimated sampling distribution generated from its use.

Acknowledgments

v

(2)

Series Editor's Introduction

vii

1. Introduction

1

(4)

1.1 The Monte Carlo Principle

3

(2)

2. Generating Individual Samples From a Pseudo-Population

5

(45)

2.1 Setting Up a Population Generating Computer Algorithm

5

(2)

2.2 Generating Single Random Variables

7

(35)

2.3 Generating Combinations of Random Variables

42

(8)

3. Using the Pseudo-Population in Monte Carlo Simulation

50

(15)

3.1 An Example of a Complete Pseudo-Population Algorithm

50

(2)

3.2 Generating a Vector of Monte Carlo Estimates

52

(3)

3.3 Generating Multiple Experiments

55

(1)

3.4 Which Statistic Is to Be Saved From a Trial?

56

(1)

3.5 How Many Trials Are Needed?

57

(2)

3.6 Evaluating Monte Carlo Estimates of Sampling Distributions

59

(6)

4. Using Monte Carlo Simulation in the Social Sciences

65

(27)

4.1 Inference When Weak Statistical Theory Exists for an Estimator

66

(6)

4.2 Testing a Null Hypothesis Under a Variety of Plausible Conditions

72

(5)

4.3 Assessing the Quality of an Inference Method

77

(5)

4.4 Assessing the Robustness of Parametric Inference to Assumption Violations

82

(6)

4.5 Comparing Estimators' Properties

88

(4)

5. Conclusion

92

(5)

Notes

97

(2)

References

99

(4)

About the Author

103

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Monte Carlo Simulation

Summary

Table of Contents

Supplemental Materials