We use cookies to enhance your experience on our website. By continuing to use our website, you are agreeing to our use of cookies. You can change your cookie settings at any time. Find out more

E-book purchase
Choose a subscription

Downloaded copy on your device does not expire. Includes 4 years of Bookshelf Online.


Where applicable, tax will be added to the above price prior to payment.

E-book purchasing help


Understanding and Using Statistics for Criminology and Criminal Justice

Jonathon A. Cooper, Peter A. Collins, and Anthony Walsh

Publication Date - 15 September 2015

ISBN: 9780199364466

400 pages
7-1/2 x 9-1/4 inches

In Stock

Addressing questions like "Why do we use a particular technique?" and "How do these numbers translate back to the real world?," this accessible text explains statistics at the conceptual level before presenting math to students


Understanding and Using Statistics for Criminology and Criminal Justice shows students how to critically examine the use and interpretation of statistics, covering not only the basics but also the essential probabilistic statistics that students will need in their future careers. Taking a conceptual approach, this unique text introduces students to the mindset of statistical thinking. It presents formulas in a step-by-step manner; explains the techniques using detailed, real-world examples; and encourages students to become insightful consumers of research.


* Assumes minimal knowledge of math and is accessible to students at all levels

* Incorporates examples from real journals, showing how statistics are used in practice

* Explains the purpose of hypothesis testing more clearly than any other text, clarifying the concept of probability and its relationship to statistics

* Call-out boxes provide more in-depth explanations of concepts

About the Author(s)

Jonathon A. Cooper is Assistant Professor of Criminology and Criminal Justice at Indiana University of Pennsylvania.

Peter A. Collins is Assistant Professor of Criminal Justice at Seattle University.

Anthony Walsh is Professor of Criminal Justice at Boise State University.


"The writing style is engaging and simple to follow. The approach adds a dimension to understanding and applying statistics to criminal justice that is typically not found in most textbooks."--Arthur Hayden, Kentucky State University

"I love the criminal justice examples. Cooper, Collins, and Walsh make the book relevant to students."--Ayana Conway, Virginia State University

Table of Contents

    Chapter 1. Introduction to Statistical Analysis
    Learning Objectives
    Why Study Statistics?
    Thinking Statistically
    Descriptive and Inferential Statistics
    Box 1-1. Galton's Quincux
    Statistics and Error
    Box 1-2. How do we know the drop in crime really happened?
    --Validity and Reliability
    --Dependent and Independent Variables
    --Nominal Level
    --Ordinal Level
    --Interval Level
    --Ratio Level
    The Role of Statistics in Science
    Box 1-3. The inductive process
    Chapter 2. Presenting Data
    Learning Objectives
    Standardizing Data
    Box 2-1. Coding data
    Box 2-2. When to use N and n
    -- Percentages
    Box 2-3. The difference between a rate and a ratio
    Box 2-4. A cautionary note
    Visualizing Data
    --Bar Charts
    --Pie Charts
    --Line Charts
    Frequency Distributions
    Box 2-5. The difference between a bar chart and a histogram
    Chapter 3. Central Tendency and Dispersion
    Learning Objectives
    Measures of Central Tendency
    --The Mean
    --Choosing a Measure of Central Tendency
    --A Research Example
    Measures of Dispersion
    --The Sum of Squares, Variance, and the Standard Deviation
    Box 3-1. N or n?
    Computational Formula for s
    More on Variability and Variance
    Box 3-2. The coefficient of variation and the index of qualitative variation
    Journal Table 3-1. Descriptive Statistics
    Chapter 4. Probability and the Normal Curve
    Learning Objectives
    --The Multiplication Rule
    --The Addition Rule
    Box 4-1. When to multiply or add probabilities?
    --A Research Example
    Theoretical Probability Distributions
    Box 4-2. What to do with 0!
    Box 4-3. Do you have a "fair coin" or not?
    --The Normal Curve
    --The Standard Normal Curve
    Z Scores
    Practical Application: The Normal Curve and z Scores
    Chapter 5. The Sampling Distribution and Estimation Procedures
    Learning Objectives
    --Simple Random Sampling
    --Stratified Random Sampling
    The Sampling Distribution
    Box 5-1. The central limit theorem
    --The Standard Error of the Sampling Distribution
    Box 5-2. Types of estimates
    Confidence Intervals and Alpha Levels
    --Calculating Confidence Intervals
    --Confidence and Precision
    --Sampling and Confidence Intervals
    Estimating Sample Size
    Practice Application: The Sampling Distribution and Estimation
    Chapter 6. Hypothesis Testing: Interval/Ratio Data
    Learning Objectives
    The Logic of Hypothesis Testing
    Errors in Hypothesis Testing
    One Sample Z Test
    The t Test
    --Directional Hypotheses: One- and Two-tailed Tests
    --Computing t
    --The Effects of Increasing Sample Size
    --Placing Confidence Intervals around t
    --T-test for Correlated (Dependent) Means
    --Calculating t with Unequal Variances
    Statistical vs. Substantive Significance, and Strength of Association
    Large Sample t Test: A Computer Example
    Journal Table 6-1. Hypothesis testing
    Practice Application: t Test
    Chapter 7. Analysis of Variance
    Learning Objectives
    Assumptions of Analysis of Variance
    The Basic Logic of ANOVA
    The Idea of Variance Revisited
    Box 7-1. The grand mean
    ANOVA and the F Distribution
    Calculating ANOVA
    Box 7-2. Calculating SSwithin
    Box 7-3. Reading the F table
    Box 7-4. Eta squared
    --Multiple Comparisons: The Scheffé Test
    Box 7-5. The advantage of ANOVA over multiple tests
    Two-Way Analysis of Variance
    --Understanding Interaction
    --A Research Example of a Significant Interaction Effect
    Journal Table 7-1. ANOVA
    Practice Application: ANOVA
    Chapter 8. Hypothesis Testing with Categorical Data: Chi square
    Learning Objectives
    Table Construction
    --Putting Percentages in Tables
    Assumptions of the Use of Chi square
    Box 8-1. Yate's correction for continuity
    The Chi square Distribution
    Chi square with a 3 x 2 Table
    Box 8-2. The relationship between z, t, F, and chi square
    Chi square-based Measures of Association
    Box 8-3. More on phi
    --Sample Size, Chi square, and phi
    --Other Measures of Association for Chi square: Contingency Coefficient; Cramer's V
    A Computer Example of Chi square
    Journal Table 8-1. Cross-tabulations and chi square
    Practice Application: Chi square
    Chapter 9. Non-parametric Measures of Association
    Learning Objectives
    Establishing Association
    --Does an Association Exist?
    --What is the Strength of the Association?
    --What is the Direction of the Association?
    Proportional Reduction in Error
    The Concept of Paired Cases
    Box 9-1. Different types of pairs for any data set
    --A Computer Example
    --Somer's d
    Tau b
    The Odds Ratio and Yule's Q
    Box 9-2. The odds and probability
    Spearman's Rank Order Correlation
    Which Test of Association Should We Use?
    Journal Table 9-1. Non-parametric measures of association
    Practice Application: Nonparametric Measures of Association
    Chapter 10. Elaboration of Tabular Data and the Nature of Causation
    Learning Objectives
    Criteria for Causality
    --Temporal Order
    Box 10-1. Variables versus constants
    Necessary and Sufficient Causes
    Multivariate Contingency Analysis
    Explanation and Interpretation
    Illustrating Elaboration Outcomes
    Box 10-2. Replication and specification
    --Controlling for One Variable
    Box 10-3. Simpson's Paradox
    --Further Elaboration: Two Control Variables
    --Partial Gamma
    Box 10-4. When not to compute partial gamma
    Problems with Tabular Elaboration
    Practice Application: Bivariate Elaboration
    Chapter 11. Bivariate Correlation and Regression
    Learning Objectives
    Linear Relationships
    Box 11-1. The scatterplot
    --Linearity in Social Science Data
    The Pearson Correlation Coefficient (r)
    Box 11-2. Calculating covariance
    --r squared as a Proportionate Reduction in Error
    --Significance Testing for Pearson's r
    Box 11-3. Standard error of r
    The Interrelationship of b, r, and ?
    Box 11-4. Summarizing the properties of r, b, and ?
    Standard Error of the Estimate
    A Computer Example of Bivariate Correlation and Regression
    Journal Table 11-1. Bivariate correlation
    Practice Application: Bivariate Correlation and Regression
    Chapter 12. Multivariate Regression and Regression
    Learning Objectives
    Partial Correlation
    Computer Example
    Second-order Partials: Controlling for Two Independent Variables
    The Multiple Correlation Coefficient
    Multiple Regression
    A Computer Example of Multiple Regression
    --Interpreting the Printout
    Box 12-1. The adjusted R squared
    Box 12-2. The y-intercept
    --A Visual Representation of Multiple Regression
    Regression and Interaction
    Journal Table 12-1. OLS regression
    Practice Application: Partial Correlation
    Appendix A: Introduction to Regression with Categorical and Limited Dependent Variables
    The Generalized Linear Model
    Binary Outcomes: The Logit
    Box A-1. About the pseudo-R squared
    Nominal Outcomes: The Multinomial Model
    Box A-2. What about the reference category?
    Ordinal Outcomes: The Ordered Logit
    Count Outcomes: Heavily Skewed Distributions
    Appendix B: A Brief Primer on Statistical Software
    Distribution Tables
    Distribution of t
    Distribution of F
    Distribution of Chi square
    Formula Index
    Subject Index