Monte Carlo Simulation is a method of evaluating substantive hypotheses and statistical estimators by developing a computer algorithm to simulate a population, drawing multiple samples from this pseudo-population, and evaluating estimates obtained from these samples. Christopher Z. Mooney explains the logic behind Monte Carlo Simulation 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, Mooney carefully demonstrates how to prepare computer algorithms using GAUSS code and illustrates these principles using several research examples.
is a method of evaluating substantive hypotheses and statistical estimators by developing a computer algorithm to simulate a population, drawing multiple samples from this pseudo-population, and evaluating estimates obtained from these samples. Christopher Z. Mooney explains the logic behind 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, Mooney carefully demonstrates how to prepare computer algorithms using GAUSS code and illustrates these principles using several research examples.Monte Carlo Simulation will enable researchers to effectively execute Monte Carlo Simulation and to interpret the estimated sampling distribution generated from its use.
Table of Content
Introduction
Generating Individual Samples from a Pseudo-Population
Using the Pseudo-Population in Monte Carlo Simulation
Using Monte Carlo Simulation in the Social Sciences
Conclusion
About the author
Christopher Z. Mooney is a professor of political studies with a joint appointment in the Institute of Government and Public Affairs.
Mooney studies U.S. state politics and policy, with special focus on legislative decision making, morality policy, and legislative term limits.
He is the founding editor of State Politics and Policy Quarterly, the premier academic journal in its field and has published dozens of articles and books, including Lobbying Illinois – How You Can Make a Difference in Public Policy.
Prior to arriving at UIS in 1999, he taught at West Virginia University and the University of Essex in the United Kingdom
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