Basic Statistics in Criminology and Criminal Justice

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

Overview

This introductory textbook takes a building-block approach that emphasizes the application and interpretation of statistics in research in crime and justice. This text is meant for both students and professionals who want to gain a basic understanding of common statistical methods used in criminology and criminal justice before advancing to more complex statistical analyses in future volumes.

This book emphasizes comprehension and interpretation. As the statistical methods discussed become more complex and demanding to compute, it integrates statistical software. It provides readers with an accessible understanding of popular statistical programs used to examine real-life crime and justice problems (including SPSS, Stata, and R). In addition, the book includes supplemental resources such as a glossary of key terms, practice questions, and sample data.

Basic Statistics in Criminology and Criminal Justice aims to give students and researchers a core understanding of statistical concepts and methods that will leave them with the confidence and tools to tackle the statistical problems in their own research work.

Product Details

ISBN-13:	9783030479695
Publisher:	Springer International Publishing
Publication date:	02/24/2021
Edition description:	5th ed. 2020
Pages:	612
Product dimensions:	7.01(w) x 10.00(h) x (d)

About the Author

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 Skholm 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.

David B. Wilson is a Professorin the Criminology, Law and Society Department at George Mason University in Virginia. He is a social psychologist and leading applied statistician in the field of criminology, and was the recipient of the Mosteller Award from the Campbell Collaboration for his contributions to the science of systematic review and meta-analysis.

Alese Wooditch is an Assistant Professor of Criminal Justice at Temple University. She received her PhD from George Mason University. Professor Wooditch is interested in innovative spatial statistical analyses in the area of criminology and criminal justice experimental and computational criminology, and quantitative methodological issues.

Table of Contents

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