Bibliograph

Advanced Statistics in Criminology and Criminal Justice, by David Weisburd, David B. Wilson, Alese Wooditch, Chester Britt

Type

Label

Advanced Statistics in Criminology and Criminal Justice, by David Weisburd, David B. Wilson, Alese Wooditch, Chester Britt

Language

eng

resource.imageBitDepth

Literary Form

non fiction

Main title

Advanced Statistics in Criminology and Criminal Justice

Medium

electronic resource

Nature of contents

dictionaries

Oclc number

1281173906

Responsibility statement

by David Weisburd, David B. Wilson, Alese Wooditch, Chester Britt

Series statement

Springer eBooks.

Summary

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 design studies. 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.--, Provided by publisher

Table Of Contents

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

Contributor

Creator