Bootstrapping, a computational nonparametric technique for "re-sampling," enables researchers to draw a conclusion about the characteristics of a population strictly from the existing sample rather than by making parametric assumptions about the estimator. Using real data examples from per capita personal income to median preference differences between legislative committee members and the entire legislature, Mooney and Duval discuss how to apply bootstrapping when the underlying sampling distribution of the statistics cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, they show the advantages and limitations of four bootstrap confidence interval methods: normal approximation, percenti

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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