Introduction to Statistics in Criminal Justice and Criminology

Nyhet

A Practical Approach to Calculating, Using, and Interpreting Data

Häftad, Engelska, 2026

AvArthur J. Lurigio,Michael Perry,Nathan Lutz,George K. Thiruvathukal

1 199 kr

Kommande

An Accessible, Scenario-Driven Foundation for Statistics in Criminal Justice A solid understanding of statistics is essential for interpreting research, evaluating evidence, and making informed decisions across the social sciences. Introduction to Statistics in Criminal Justice and Criminology offers a clear and structured path to the tools and reasoning that shape empirical inquiry in the field. Emphasizing the practical purposes of statistical thinking, this student-friendly textbook explains how data is organized, described, compared, and analyzed to answer meaningful questions. Each chapter opens with a relatable scenario that frames key concepts, guiding students as they move from foundational topics—such as descriptive statistics and normal distributions—to applications including hypothesis testing, chi-square analysis, regression, ANOVA, and survival analysis. Step-by-step examples, end-of-chapter problems, and intuitive visual displays help students engage more deeply with empirical scholarship and apply a range of data-driven methods. By presenting statistics as both a practical toolkit and an essential mode of reasoning, this comprehensive guide: Balances conceptual understanding with applied calculation to support success in higher-level courseworkProvides a consistent framework that connects chapter content to decision-making and research designUses clear explanations to demystify core statistical ideas and frequently misunderstood conceptsStrengthens computational literacy through a user-friendly software tool aligned with the chapter materialOffers chapter summaries, structured practice opportunities, and extendable problem setsDraws on the authors' extensive experience in criminal justice, psychology, mathematics, statistics, and computer scienceLeverages the expertise of an interdisciplinary team spanning psychology, computer science, data science, and statistics.Requiring no advanced mathematical training, Introduction to Statistics in Criminal Justice and Criminology: A Practical Approach to Calculating, Using, and Interpreting Data is ideal for undergraduate and postgraduate courses such as Statistics in Criminal Justice, Introduction to Statistics in Criminal Justice, and Statistics for the Social Sciences. It supports required quantitative training across criminal justice curricula and prepares students in bachelor’s and graduate programs to interpret research and engage in data-informed study and practice.

Produktinformation

Utgivningsdatum2026-04-29
FormatHäftad
SpråkEngelska
Antal sidor304
Upplaga26001
FörlagJohn Wiley & Sons Inc
ISBN9781118559239

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Brottslighet och kriminologi inom Samhälle och politik

Arthur J. Lurigio is Professor of Psychology and Criminal Justice and Criminology at Loyola University Chicago. Holding a PhD from Loyola University Chicago, he is a distinguished scholar with more than 500 publications and numerous awards recognizing his contributions to criminal justice research, mental health, and applied scholarship. Michael Perry is Senior Lecturer of Mathematics and Statistics at Loyola University. Holding a PhD in Engineering Science and Applied Mathematics from Northwestern University, he teaches a range of statistics courses and has published research across polymerization, public health, and quantitative modeling. Nathan M. Lutz is a pediatric psychologist and Clinical Assistant Professor at Nationwide Children’s Hospital in Columbus, Ohio. Holding a PhD in Clinical Psychology from Loyola University Chicago, his research focuses on improving outcomes for youth involved in child welfare and enhancing measurement-based behavioral health care. George K. Thiruvathukal is Professor and Chairperson of Computer Science at Loyola University Chicago and Visiting Computer Scientist at Argonne National Laboratory. Holding a PhD in Computer Science from the Illinois Institute of Technology, his research spans high-performance computing, distributed systems, empirical software engineering, artificial intelligence/machine learning, history of computing/science/technology and digital humanities/music. He is author/co-author of more than 200 publications and 6 books.

About the Authors xiiiAcknowledgements xv1 Introduction 11.1 Statistics 11.2 Types of Statistics: Descriptive and Inferential 11.3 Basic Statistical Concepts and Terminology 21.4 Dependent and Independent Variables 101.5 Practical Application of Statistics 111.6 Introduction to SAS 111.7 Summary 121.8 Exercises 121.9 Answers to Exercises 142 Organizing and Describing Data 172.1 The Jail Report 172.2 Organizing and Describing Data 172.3 Frequency Distributions 182.4 Visual Techniques 272.5 Properties of Distributions 372.6 Summary 412.7 Exercises 422.8 Answers to Exercises 433 Comparative Statistics 483.1 Re-electing the County Sheriff 483.2 Trend Analyses 543.3 Summary 613.4 Importing Data from a Database in SAS 623.5 Exercises 623.6 Answers to Exercises 654 Descriptive Statistics: Measures of Central Tendency and Variability 694.1 Analysis of Domestic Violence Cases 694.2 Measures of Central Tendency 694.3 Central Tendency 704.4 The Mean 704.5 The Median 754.6 The Mode 764.7 When to Use Measures of Central Tendency: A FinalWord 834.8 Characteristics of the Mean and Median 844.9 Variability 854.10 The Range 864.11 Measures of Deviation 874.12 Variance 874.13 Standard Deviation 884.14 Calculating Central Tendencies and Variability in SAS 904.15 Summary 934.16 Exercises 934.17 Answers to Exercises 955 Normal Distributions 985.1 Theoretical Distributions 985.1.1 Characterizing Shapes 985.2 Normal Distribution PDF 995.3 Binomial Distribution PMF 1005.4 The Standard Normal Distribution 1045.5 Properties of Z-Scores 1065.6 Summary 1165.7 Exercises 1165.8 Answers to Exercises 1176 Hypothesis Testing: z- and t-Tests 1196.1 Introduction 1196.2 Hypothesis Testing, One Sample z 1206.3 Hypothesis Testing, One Sample t 1316.4 Summary 1436.5 Exercises 1446.6 Answers to Exercises 1457 Analyzing Categorical Data: Chi-Square Test 1527.1 Comparing Proportions 1527.2 Comparing Proportions – Two Samples 1557.3 The Chi-Square Test 1567.4 Summary 1667.5 Exercises 1667.6 Answers to Exercises 1678 Correlation Coefficient 1768.1 Relationships Between Variables 1768.2 Interpreting the Meaning and Significance of r 1858.3 Summary 1898.4 Exercises 1938.5 Answers to Exercises 1939 Linear Regression and Prediction 1969.1 The Concept of Prediction 1969.2 Basic Assumptions and Terminology 1979.3 Linear Regression Using SAS 2039.4 Multiple Linear Regression 2119.5 Summary 2189.6 Exercises 2189.7 Answers to Exercises 21910 Analysis of Variance 22310.1 Introduction 22310.2 A Return to Variance 22410.3 Steps in Hypothesis Testing 22910.4 ANOVA in SAS 23710.5 Summary 23910.6 Exercises 23910.7 Answers to Exercises 24111 Survival Analysis 24911.1 Introduction 24911.2 Survival Function 25011.3 Summary 26311.4 Exercises 26311.5 Answers to Exercises 265Appendices 268Index 295