Handbook of quantitative criminology

書誌事項

Handbook of quantitative criminology

Alex R. Piquero, David Weisburd, editors

Springer, c2010

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

Quantitative criminology has certainly come a long way since I was ?rst introduced to a largely qualitative criminology some 40 years ago, when I was recruited to lead a task force on science and technology for the President's Commission on Law Enforcement and Administration of Justice. At that time, criminology was a very limited activity, depending almost exclusively on the Uniform Crime Reports (UCR) initiated by the FBI in 1929 for measurement of crime based on victim reports to the police and on police arrests. A ty- cal mode of analysis was simple bivariate correlation. Marvin Wolfgang and colleagues were makingan importantadvancebytrackinglongitudinaldata onarrestsin Philadelphia,an in- vation that was widely appreciated. And the ?eld was very small: I remember attending my ?rst meeting of the American Society of Criminology in about 1968 in an anteroom at New York University; there were about 25-30 people in attendance, mostly sociologists with a few lawyers thrown in. That Society today has over 3,000 members, mostly now drawn from criminology which has established its own clear identity, but augmented by a wide variety of disciplines that include statisticians, economists, demographers, and even a few engineers. This Handbook provides a remarkable testimony to the growth of that ?eld. Following the maxim that "if you can't measure it, you can't understand it," we have seen the early dissatisfaction with the UCR replaced by a wide variety of new approaches to measuring crime victimization and offending.

目次

Introduction I. Topics in Research Design 1). Experiments - trials 2). Experiments - block/randomized and subgroup 3). Propensity scores 4). Regression discontinuity designs 5) Quantitative and Qualitative Data 6) Statistical power II) Methods for Overcoming Data Limitations 7) Data reliability and data comparisons 8) Missing data III) Innovative Descriptive Methods 10) Geographic mapping of crime 11) Visualizing data 12) Trajectories 13) Growth curve models IV) Estimation Techniques for Theory and Policy 14) Estimating Costs of Crime 15) Estimating treatment effects 16) Meta-analysis V) Topics in Multiple Regression 17) Instrumental variables 18) Multilevel modeling 19) Logic and related extensions 20) Count models VI) New Directions in Statistical Analysis 21) Geographic statistical analysis of crime 22) Data mining 23) Time series 24) Network analysis Conclusion

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