Advanced Statistics in Criminology and Criminal Justice
Beschreibung:
David Weisburd is a leading researcher and scholar in criminology and criminal justice. He is Distinguished Professor of Criminology, Law and Society at George Mason University in Virginia and Walter E. Meyer Professor of Law and Criminal Justice at the Hebrew University of Jerusalem. Professor Weisburd has received many awards and prizes for his contributions to criminology and criminal justice including the Stockholm Prize in Criminology and the Sutherland and Vollmer Awards from the American Society of Criminology.
Chester Britt was a leading researcher and scholar in the field of criminology. During his career, he taught at a number of universities and led departments at Northeastern University, Arizona State University, and the University of Iowa. His research addressed theories of criminal behavior and victimization, demography of crime and criminal careers, criminal justice decision-making, and quantitative research methods.This book provides the student, researcher or practitioner with the tools to understand many of the most commonly used advanced statistical analysis tools in criminology and criminal justice, and also to apply them to research problems.
The volume is structured around two main topics, giving the user flexibility to find what they need quickly. The first is "the general linear model" which is the main analytic approach used to understand what influences outcomes in crime and justice. It presents a series of approaches from OLS multivariate regression, through logistic regression and multi-nomial regression, hierarchical regression, to count regression. The volume also examines alternative methods for estimating unbiased outcomes that are becoming more common in criminology and criminal justice, including analyses of randomized experiments and propensity score matching. It also examines the problem of statistical power, and how it can be used to better designstudies. Finally, it discusses meta analysis, which is used to summarize studies; and geographic statistical analysis, which allows us to take into account the ways in which geographies may influence our statistical conclusions.
Chapter 1. Introduction.- Chapter 2. Multiple Regression- Chapter 3. Multiple Regression: Additional Topics.- Chapter 4. Logistic Regression.- Chapter 5. Multivariate Regression With Multiple Category Nominal or Ordinal Measures.- Chapter 6. Count-Based Regression Models.- Chapter 7. Multilevel Regression Models.- Chapter 8. Statistical Power.- Chapter 9. Special Topics: Randomized Experiments.- Chapter 10. Propensity Score Matching.- Chapter 11. Meta-Analysis.- Chapter 12. Spatial Regression.