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Monte Carlo Simulation by Christopher Z. Mooney


Monte Carlo Simulation by Christopher Z. Mooney

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. The author explains the logic behind the method and demonstrates its uses for social and behavioural 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.

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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Book Info

Publication date

7th April 1997


Christopher Z. Mooney

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SAGE Publications Inc


112 pages


Game theory
Probability & statistics



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