Understanding and Using Statistics for Criminology and Criminal Justice by Jonathon A. CooperUnderstanding and Using Statistics for Criminology and Criminal Justice by Jonathon A. Cooper

Understanding and Using Statistics for Criminology and Criminal Justice

byJonathon A. Cooper, Peter A. Collins, Anthony Walsh

Paperback | September 29, 2015

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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 aconceptual 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.
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.
Title:Understanding and Using Statistics for Criminology and Criminal JusticeFormat:PaperbackDimensions:400 pages, 9.21 × 7.4 × 0.59 inPublished:September 29, 2015Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:019936446X

ISBN - 13:9780199364466

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Table of Contents

PrefacePART 1. THE BUILDING BLOCKS OF PROBABILISTIC STATISTICS1. Introduction to Statistical AnalysisLearning ObjectivesWhy Study Statistics?Thinking StatisticallyDescriptive and Inferential StatisticsBox 1-1. Galton's QuincuxStatistics and ErrorBox 1-2. How do we know the drop in crime really happened?Operationalization--Validity and ReliabilityVariables--Dependent and Independent Variables--Nominal Level--Ordinal Level--Interval Level--Ratio LevelThe Role of Statistics in ScienceBox 1-3. The inductive process2. Presenting DataLearning ObjectivesIntroductionStandardizing Data--CountsBox 2-1. Coding dataBox 2-2. When to use N and n-- Percentages--RatesBox 2-3. The difference between a rate and a ratioBox 2-4. A cautionary noteVisualizing Data--Bar Charts--Pie Charts--Line ChartsFrequency DistributionsBox 2-5. The difference between a bar chart and a histogram3. Central Tendency and DispersionLearning ObjectivesIntroductionMeasures of Central Tendency--Mode--Median--The Mean--Choosing a Measure of Central Tendency--A Research ExampleMeasures of Dispersion--Range--The Sum of Squares, Variance, and the Standard DeviationBox 3-1. N or n?Computational Formula for sMore on Variability and VarianceBox 3-2. The coefficient of variation and the index of qualitative variationJournal Table 3-1. Descriptive Statistics4. Probability and the Normal CurveLearning ObjectivesProbability--The Multiplication Rule--The Addition RuleBox 4-1. When to multiply or add probabilities?--A Research ExampleTheoretical Probability DistributionsBox 4-2. What to do with 0!Box 4-3. Do you have a "fair coin" or not?--The Normal Curve--The Standard Normal CurveZ ScoresPractical Application: The Normal Curve and z Scores5. The Sampling Distribution and Estimation ProceduresLearning ObjectivesSampling--Simple Random Sampling--Stratified Random SamplingThe Sampling DistributionBox 5-1. The central limit theorem--The Standard Error of the Sampling DistributionBox 5-2. Types of estimatesConfidence Intervals and Alpha Levels--Calculating Confidence Intervals--Confidence and Precision--Sampling and Confidence IntervalsEstimating Sample SizePractice Application: The Sampling Distribution and Estimation6. Hypothesis Testing: Interval/Ratio DataLearning ObjectivesIntroductionThe Logic of Hypothesis TestingErrors in Hypothesis TestingOne Sample Z TestThe 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 VariancesStatistical vs. Substantive Significance, and Strength of AssociationLarge Sample t Test: A Computer ExampleJournal Table 6-1. Hypothesis testingPractice Application: t TestPART 2. HYPOTHESIS TESTING WITH PROBABILISTIC STATISTICS7. Analysis of VarianceLearning ObjectivesIntroductionAssumptions of Analysis of VarianceThe Basic Logic of ANOVAThe Idea of Variance RevisitedBox 7-1. The grand meanANOVA and the F DistributionCalculating ANOVABox 7-2. Calculating SSwithinBox 7-3. Reading the F tableBox 7-4. Eta squared--Multiple Comparisons: The Scheffe TestBox 7-5. The advantage of ANOVA over multiple testsTwo-Way Analysis of Variance--Understanding Interaction--A Research Example of a Significant Interaction EffectJournal Table 7-1. ANOVAPractice Application: ANOVA8. Hypothesis Testing with Categorical Data: Chi squareLearning ObjectivesIntroductionTable Construction--Putting Percentages in TablesAssumptions of the Use of Chi squareBox 8-1. Yate's correction for continuityThe Chi square DistributionChi square with a 3 x 2 TableBox 8-2. The relationship between z, t, F, and chi squareChi square-based Measures of AssociationBox 8-3. More on phi--Sample Size, Chi square, and phi--Other Measures of Association for Chi square: Contingency Coefficient; Cramer's VA Computer Example of Chi squareJournal Table 8-1. Cross-tabulations and chi squarePractice Application: Chi square9. Non-parametric Measures of AssociationLearning ObjectivesIntroductionEstablishing Association--Does an Association Exist?--What is the Strength of the Association?--What is the Direction of the Association?Proportional Reduction in ErrorThe Concept of Paired CasesBox 9-1. Different types of pairs for any data set--A Computer Example--Gamma--Lambda--Somer's dTau bThe Odds Ratio and Yule's QBox 9-2. The odds and probabilitySpearman's Rank Order CorrelationWhich Test of Association Should We Use?Journal Table 9-1. Non-parametric measures of associationPractice Application: Nonparametric Measures of Association10. Elaboration of Tabular Data and the Nature of CausationLearning ObjectivesIntroductionCriteria for Causality--Association--Temporal Order--SpuriousnessBox 10-1. Variables versus constantsNecessary and Sufficient CausesMultivariate Contingency AnalysisExplanation and InterpretationIllustrating Elaboration OutcomesBox 10-2. Replication and specification--Controlling for One VariableBox 10-3. Simpson's Paradox--Further Elaboration: Two Control Variables--Partial GammaBox 10-4. When not to compute partial gammaProblems with Tabular ElaborationPractice Application: Bivariate Elaboration11. Bivariate Correlation and RegressionLearning ObjectivesIntroductionLinear RelationshipsBox 11-1. The scatterplot--Linearity in Social Science DataThe Pearson Correlation Coefficient (r)Box 11-2. Calculating covariance--r squared as a Proportionate Reduction in Error--Significance Testing for Pearson's rBox 11-3. Standard error of rThe Interrelationship of b, r, and ?Box 11-4. Summarizing the properties of r, b, and ?Standard Error of the EstimateA Computer Example of Bivariate Correlation and RegressionJournal Table 11-1. Bivariate correlationPractice Application: Bivariate Correlation and Regression12. Multivariate Regression and RegressionLearning ObjectivesIntroductionPartial CorrelationComputer ExampleSecond-order Partials: Controlling for Two Independent VariablesThe Multiple Correlation CoefficientMultiple RegressionA Computer Example of Multiple Regression--Interpreting the PrintoutBox 12-1. The adjusted R squaredBox 12-2. The y-intercept--A Visual Representation of Multiple RegressionRegression and InteractionJournal Table 12-1. OLS regressionPractice Application: Partial CorrelationAppendix A: Introduction to Regression with Categorical and Limited Dependent VariablesThe Generalized Linear ModelBinary Outcomes: The LogitBox A-1. About the pseudo-R squaredNominal Outcomes: The Multinomial ModelBox A-2. What about the reference category?Ordinal Outcomes: The Ordered LogitCount Outcomes: Heavily Skewed DistributionsAppendix B: A Brief Primer on Statistical SoftwareSPSSSASStataRConclusionsDistribution TablesDistribution of tDistribution of FDistribution of Chi squareGlossaryFormula IndexSubject Index

Editorial Reviews

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