Statistical Analysis in Criminal Justice and Criminology: A User's Guide

by ;
Edition: 1st
Format: Hardcover
Pub. Date: 2002-01-01
Publisher(s): Pearson College Div
List Price: $66.78

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Summary

Statistical Analysis in Criminal Justice and Criminology is aimed at undergraduates with little or no background in the subject. It is premised on active learning utilizing common statistical tools to analyze criminal justice data. The focus is on the student's understanding of fundamental statistical analysis, and not on comprehensive coverage of statistical research. Special features of the book include: bull; bull;examples of criminal justice research bull;step-by-step guidance in using SPSS bull;two data sets: a) crime data from the Uniform Crime Reports for all 50 states, coupled with other data, such as number of executions, size of the jail and prison population; and b) survey data from the National Survey on Crime and Justice. bull;Companion Website, an instructor's manual, and a student study guide introduction of a new method of analysis in each chapter, along with terms, examples, and practice problems.

Table of Contents

Preface xi
The Purpose of Statistical Analysis
1(25)
Chapter Overview
1(1)
The Validity and Reliability of Crime Statistcs
2(12)
The Uniform Crime Report
2(3)
The National Incident-Based Reporting System
5(3)
The National Crime Victimization Survey
8(4)
Calls for Service Data
12(1)
Routine Activities Theory and Crime ``Hot Spots''
13(1)
Statistics in Action: The Compstat Program
14(2)
Percentage Trend Analysis
16(2)
Lying With Statics
18(1)
Conclusion
19(7)
Key Terms
20(1)
Exercises
20(4)
Notes
24(2)
Basic Elements of Criminal Justice Research
26(10)
Chapter Overview
26(1)
Elements of the Research Process
27(6)
Theory, Hypotheses, and Variables
27(1)
The Research Process
27(5)
Program Evaluation
32(1)
Conclusion
33(3)
Key Terms
34(1)
Exercises
35(1)
Notes
35(1)
Summarizing Data and Presenting the Results
36(12)
Chapter Overview
36(1)
Frequency Distributions
36(2)
Displaying Frequency Distributions Graphically
38(5)
Bar Charts
39(1)
Pie Charts
39(3)
Histograms
42(1)
Line Graphs
43(1)
Presenting Your Results
43(3)
Conclusion
46(2)
Key Terms
46(1)
Data Analysis
46(2)
Measures of Central Tendency
48(19)
Chapter Overview
48(1)
Measures of Central Tendency
48(5)
Constructing a Frequency Distribution
48(2)
The Mode
50(1)
The Median
50(1)
The Mean
51(1)
Skewed Distributions
52(1)
Levels of Measurement
53(4)
The Nominal Level
54(1)
The Ordinal Level
54(1)
The Interval Level
55(1)
The Ratio Level
56(1)
Measurement and Types of Variables
57(1)
Using SPSS Studentware to Analyze Data
57(5)
Conclusion
62(5)
Key Terms
64(1)
Exercises
65(1)
Data Analysis
66(1)
Notes
66(1)
Measures of Dispersion
67(12)
Chapter Overview
67(1)
Measures of Dispersion
67(5)
The Range
67(1)
The Variance and Standard Deviation
68(4)
SPSS Studentware: Measures of Dispersion
72(5)
Conclusion
77(2)
Key Terms
77(1)
Exercises
77(1)
Data Analysis
78(1)
Probability and the Normal Curve
79(20)
Chapter Overview
79(1)
Introduction to Probility
79(9)
Law of Probability
80(2)
Shooting Craps
82(4)
American Indians and Crime: Reading Tables to Determine Empirical Probability
86(2)
Prbability and the Normal Curve
88(2)
The Binomial Distribution
90(1)
The Central Limit Theorem
91(2)
Establishing Confidence Intervals
93(3)
Conclusion
96(3)
Key Terms
97(1)
Exercises
98(1)
Notes
98(1)
Difference Between Means: The t-Test
99(19)
Chapter Overview
99(1)
The t Distribution
99(2)
Hypothesis Testing
101(14)
Stating Hypotheses
101(3)
Decisions Under the Null Hypothesis
104(1)
t-Test for Related Samples
105(2)
t-Test for Independent Samples
107(1)
SPSS Studentware: t-Test for Independent Samples
108(7)
Conclusion
115(3)
Key Terms
115(1)
Data Analysis
116(1)
Notes
117(1)
Analysis of Variance (One Way)
118(16)
Chapter Overview
118(1)
Source of ANOVA
118(2)
The F Test (F Ratio)
120(12)
Calculating ANOVA by Hand
121(4)
One-Way ANOVA Using SPSS Studentware
125(6)
The Bonferroni Procedure
131(1)
Conclusion
132(2)
Key Terms
132(1)
Data Analysis
132(1)
Notes
133(1)
Correlation
134(19)
Chapter Overview
134(1)
Defining Correlation
134(1)
Interpreting Correlation
135(4)
Direction of the Correlation Coefficient
136(1)
Magnitude of the Correlation Coefficient
137(1)
Percentage of Variance Explained
138(1)
Considering Causation
139(4)
Five Criteria for a Causal Relationship
139(2)
Calculating Pearson's r
141(2)
Bivariate Correlation Using SPSS Studentware
143(6)
Conclusion
149(4)
Key Terms
150(1)
Data Analysis
151(1)
Notes
151(2)
Regression
153(18)
Chapter Overview
153(1)
Defining Regression
153(13)
Using Regression: Predicting Prison population Size
154(1)
Calculating Regression Coefficients by Hand
155(5)
Linear Regression Using SPSS Studentware
160(6)
Conclusion
166(5)
Key Terms
167(1)
Data Analysis
167(3)
Notes
170(1)
Contingency Table Analysis
171(24)
Chapter Overview
171(1)
Nonparametric Statistics
171(1)
Constructing Contingency Tables
172(3)
Summary: Rules for the Construction and Interpretation of Tables
174(1)
Chi-Square Test for Independent Samples
175(2)
Calculating Chi-Square
177(4)
Chi-Square Calculation
177(1)
Calculating Chi-Square Using SPSS Studentware
178(3)
Measures of Association With Chi-Square
181(5)
Cramer's V and SPSS Studentware
185(1)
Introducing a Third Variable
186(5)
Conclusion
191(4)
Key Terms
191(1)
Data Analysis
192(1)
Notes
193(2)
The Use of Statistics in Policy Analysis
195(6)
Chapter Overview
195(1)
The Purpose of Research
195(2)
Examples of Successes and Failures
197(2)
Kansas City Preventive Patrol Experiment
197(1)
Kansas City Gun Experiment
198(1)
Capital Punishment
198(1)
Closing Comments
199(2)
Notes
200(1)
Appendix 201(33)
Index 234

Excerpts

Statistical analysis typically is a course that students dread and are likely to avoid until late in their academic careers. Teaching statistical analysis can be a particularly frustrating experience for instructors. Students fail to see the relevance of such courses, thus creating barriers of epic proportions. How can we communicate and instruct in the proper use of the tools of the trade and show students how it leads to the "fun stuff" in research? Having struggled with this thorny issue for some 30 combined years of experience teaching statistics classes in criminal justice programs, we embark on a new path. This text de-emphasizes the traditional method of teaching statistical analysis by hand calculation. Instead, we incorporate a way of interpreting data that introduces students to the way that we actually do research. Our purpose is to provide a book that is written at the proper level to instruct students in the use of statistics. We have elected to avoid a comprehensive approach common to many statistics texts. Instead, we focus on statistical tools typically used in the social sciences. We believe that the focus should be on the student's understanding of fundamental statisticalanalysis,and not on comprehensive statisticalteststhat are superfluous. In essence, the text is premised on active learning utilizing common statistical tools to analyze criminal justice data. We emphasize criminal justice examples, along with step-by-step guidance in using SPSS Studentware. We provide two data sets: (a) crime data from theUniform Crime Reportsfor all 50 states, coupled with other data, such as number of executions and size of jail and prison populations and (b) survey data from the National Survey on Crime and Justice. Exercises are based on these data sets. It is our belief that the best way to learn statistical analysis is by doing it. We also providePowerPointpresentations, on-line assistance, and other ancillary materials to help guide students as they learn a new language, a new way of thinking and problem solving, and a new way of working. The text is based on our classroom experience. We have learned the hard way what students can handle, where they get into trouble, and what type of explanations seem to part the fog of math anxiety. We express our gratitude to our reviewers: Tom Lucadamo at the University of Baltimore, Steve Holmes at the University of Central Florida, and Leon Pettiway of Indiana University. Their careful attention to the original manuscript led to many improvements. We also thank our editor, Kim Davies, for her patient attention to the production of this text and her ability to tolerate our eccentricities. Please feel free to contact us directly with questions or comments about this text at: gfvito0l@gwise.louisville.edu and blankemn@etsu.edu. Good luck with your plunge into statistical analysis! Gennaro Vito Michael Blankenship

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