Christopher Z. Mooney – författare

Legislative term limits are reshaping the political landscape in numerous states; however, few of the effects are consistent across all states. Everything from the political environment to the level of legislative professionalism within a state influences the trends that are often attributed to term limits. To cut through these many trends and isolate the ones most likely created by term limits, this volume develops comparisons of states with term limits to similar states without term limits. The comparisons are organized by levels of legislative professionalism. The richness of the case study approach allows the contributors to Legislating Without Experience to offer valuable insights into the legislative process in each of the specific states. They also illuminate the individual idiosyncrasies that enhance or dilute the effects of term limits in a given state. Rarely does a case study book with multiple contributors offer apples-to-apples data comparisons. This project engaged nationally recognized scholars to collect and analyze comparable data in each state. The loss of major power brokers and their institutional memory makes the legislature a more chaotic place. Legislating Without Experience argues that on the whole, the legislature as an institution has been weakened by term limits. However, these effects vary from state to state based on the specifics of the limit and the degree of legislative professionalism. Importantly, legislative actors are adapting to the limits and making the best of a difficult situation.This book will be an excellent reference for students and scholars of state politics, legislative process, and term limits.

This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . . --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to apply this computer-intensive methodology using various available software packages.

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.

will enable researchers to effectively execute Monte Carlo Simulation and to interpret the estimated sampling distribution generated from its use.

In 1969, political scientist Jack Walker published 'The Diffusion of Innovations among the American States' in the American Political Science Review. 'Walker 1969' has since become a cornerstone of political science, packed with ideas, conjectures, and suggestions that spawned multiple lines of research in multiple fields. In good Kuhnian fashion, Walker 1969 is important less for the answers it provides than for the questions it raises, inspiring generations of political scientists to use the political, institutional, and policy differences among the states to understand policymaking better. Walker 1969 is the rock on which the modern subfield of state politics scholarship was built, in addition to inspiring copious research into federalism, comparative politics, and international relations. This Element documents the deep and extensive impact of Walker 1969 on the study of policymaking in the US states. In the process, it organizes and analyzes that literature, demonstrating its progress and promise.