Statistics in Criminal Justice

Statistics in Criminal Justice takes an approach that emphasizes the uses of statistics in research in crime and justice. This text is meant for students and professionals who want to gain a basic understanding of statistics in this field. The text takes a building-block approach, meaning that each chapter helps to prepare the student for the chapters that follow. It also means that the level of sophistication of the text increases as the text progresses. Throughout the text there is an emphasis on comprehension and interpretation, rather than computation. However, it takes a serious approach to statistics, which is relevant to the real world of research in crime and justice. This approach is meant to provide the reader with an accessible but sophisticated understanding of statistics that can be used to examine real-life criminal justice problems. The goal of the text is to give the student a basic understanding of statistics and statistical concepts that will leave the student with the confidence and the tools for tackling more complex problems on their own. Statistics in Criminal Justice is meant not only as an introduction for students but as a reference for researchers.

A number of changes have been made to the 3rd edition, including the following:

- Additional exercises at the end of each chapter

- Expanded computer exercises that can be performed in the Student Version of SPSS

- Extended discussion of multivariate regression models, including interaction and non-linear effects

- A new chapter on multinomial and ordinal logistic regression models, examined in a way that highlights comprehension and interpretation

- With the additional material on multivariate regression models, the text is appropriate for both undergraduate and beginning graduate statistics courses in criminal justice

Introduction: Statistics as a Research Tool.- Measurement: The Basic Building Block of Research.- Representing and Displaying Data.- Describing the Typical Case: Measures of Central Tendency.- How Typical Is the Typical Case?: Measuring Dispersion.- The Logic of Statistical Inference: Making Statements About Populations from Sample Statistics.- Defining the Observed Significance Level of a Test: A Simple Example Using the Binomial Distribution.- Steps in a Statistical Test: Using the Binomial Distribution to Make Decisions About Hypotheses.- Chi-Square: A Test Commonly Used for Nominal-Level Measures.- The Normal Distribution and Its Application to Tests of Statistical Significance.- Comparing Means and Proportions in Two Samples.- Comparing Means Among More Than Two Samples: Analysis of Variance.- Measures of Association for Nominal and Ordinal Variables.- Measuring Association for Interval-Level Data: Pearson's Correlation Coefficient.- An Introduction to Bivariate Regression.- Multivariate Regression.- Multivariate Regression: Additional Topics.- Logistic Regression.- Multivariate Regression with Multiple Category Nominal or Ordinal Measures: Extending the Basic Logistic Regression Model.- Special Topics: Confidence Intervals.- Special Topics: Statistical Power.

David Weisburd (Ph.D., Yale University) is a leading researcher and scholar in the field of criminal justice. He is Professor of Criminology at the Hebrew University Law School in Jerusalem and is a professor in the Department of Criminology and Criminal Justice at the University of Maryland. Professor Weisburd serves as a senior fellow at the Police Foundation in Washington DC, and is a member of the National Academy of Sciences Panel on Police Practices and Polices and the steering committee of the Campbell Crime and Justice Coordinating Group.

Chester Britt (Ph.D, University of Arizona) is a researcher and scholar in the field of criminology. He is Associate Professor in the Administration of Justice Department at Arizona State University West. Professor Britt is the editor for Justice Quarterly. He has published more than twenty scientific articles and book chapters on issues related to the demography of crime, criminal careers, criminal case processing, and statistics.