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Informationen zum Autor 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, Carsey’s 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. Carsey’s 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 . Klappentext 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 pr "[The] writing is direct and to the point...I can't underemphasize that part. Too many methods books try to soften the technical edge by throwing in lots of commentary." -Paul Johnson, Center for Research Methods and Data Analysis, University of Kansas Zusammenfassung 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. Inhaltsverzeichnis 1. Introduction 2. Probability 3. Introduction to R 4. Random Number Generation 5 .Statistical Simulation of the Linear Model 6. Simulating Generalized Linear Models 7. Testing Theory Using Simulation 8. Resampling Methods 9. Other Simulation-Based Methods 10. Final Thoughts ...
