Statistics in Criminal Justice

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.

• 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.