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Statistics in Criminology and Criminal Justice

Statistics in Criminology and Criminal Justice:

Analysis and Interpretation, Second Edition

By Jeffery T. Walker, Sean Maddan

423 pages

retail $93.95
Our Price $75.15


PI Magazine Bookstore is proud to offer investigators Statistics in Criminology and Criminal Justice. Thoroughly updated and revised, the Second Edition of Statistics in Criminology and Criminal Justice: Analysis and Interpretation provides criminal justice students with a firm knowledge base in the theory and application of statistical analyses. Students will be introduced to methods of identifying and classifying data, followed by explanations and demonstrations of statistical procedures. They will learn what statistical techniques are appropriate for particular data, why procedures give the results they do, and how to interpret the output of statistical analyses.

This edition features updated statistical output, clear explanations of how to perform the analyses being discussed, revised data files, additional data sets to increase students' ability to conduct research on their own, and extensive use of flowcharts and examples to maximize students' comprehension of the topic.

• All statistical output has been updated to newer versions of SPSS software so students using current versions of SPSS will be able to easily compare their output to the book.
• Expanded coverage of ANOVA.
• Includes a CD-ROM containing practice exercises.

Table of Contents:

1. The Logic of Comparison and Analysis
• Introduction: Why Analyze Data?
• Some Statistical History
• Use of Statistics
• Theory Construction at a Glance
- What is Theory?
- Theory and Research
• The Process of Scientific Inquiry
- Observation and Inquisitiveness
- Primary Questions
- Research Questions
• Research: Movement from Theory to Data and Back
- Formulating Hypotheses
- Constructing the Research Design
- Conceptualization
- Operationalization
- Gathering the Data
• Statistical Analysis: The Art of Making Comparisons
- Foundations of Valid Comparisons
- Comparing Appropriate Phenomena
- Using Comparable Measures
- Choosing Analysis Methods that Best Summarize the Data
- Drawing Conclusions
- Communicating the Results
• Data and the Purposes of This Book
• Chapter Resources

2. Variables and Measurement
• The Variable Defined
• Transforming Characteristics Into Data: The Process of Measurement
• How Variables Can Differ
- Levels of Measurement
- Scale Continuity
- Use in the Research Process
• Conclusions
• Chapter Resources

3. Understanding Data Through Organization: Summarizing Data in Tables, Frequency Distributions, and Graphical Representation
• Frequency Distributions: A Chart of a Different Color
- Conventions for Building Distributions
- Frequency Distribution
- Percentage Distributions
- Combination Distributions
• Graphical Representations of Frequencies
- Pie Charts
- Histograms and Bar Charts
- Polygons and Area Charts
• Analyzing Univariate Statistics
• Analyzing Change
- Line Charts
- Ogives
• Analyzing Bivariate and Mulitvariate Data
- Scatter Plots
- Normal Probability Plots
- Path Diagrams
• Analyzing Geographic Distributions
- Pin, Spot, or Point Maps
- Choropleth Maps
• Conclusion
• Chapter Resources

4. Measures of Central Tendency
• Univariate Descriptive Statistics
• Measures of Central Tendency
- Mode
- Median
- Mean
• Selecting the Most Appropriate Measure of Central Tendency
• Conclusion
• Chapter Resources

5. Measures of Dispersion
• Deviation of Dispersion
• Measures of Dispersion
- Range
- Index of Dispersion
- Mean Absolute Deviation
- Variance
- Standard Deviation
- Uses for the Variance and Standard Deviation
• Selecting the Most Appropriate Measure of Dispersion
• Conclusion
• Chapter Resources

6. The Form of Distribution
• Moments of a Distribution
• Number of Modes
• Skewness
- Analysis of Skew
• Kurtosis
- Analysis of Kurtosis
• The Importance of Skew and Kurtosis
• Design of the Normal Curve
- Points to Remember About the Normal Curve
• Conclusion
• Chapter Resources

7. Introduction to Bivariate Descriptive Statistics
• Bivariate Tables and Analysis
- Statistical Tables versus Presentation Tables
• Constructing Bivariate Tables
- Ordinal Level Table Construction
- Nominal Level Table Construction
• Analysis of Bivariate Tables
• Conclusion
• Chapter resources

8. Measures of Existence and Statistical Significance
• Nominal Level Measures of Existence
• Tables, Percentages, and Differences
• Chi-Square
- Requirements of Using Chi-Square
- Limitations of Chi-Square
- Final Note on Chi-Square
• Tests of Existence for Ordinal and Interval Level Data
- Calculation and Interpretation for Ordinal Data
- Spearman's Rho and Pearson's r
• An Issue of Significance
• Conclusion
• Chapter resources

9. Measures of Strength of a Relationship
• What Is Association?
• Nominal Level Data
• Ordinal Level Data
- Tau
- Gamma
- Somers' d
- Spearman's Rho
• Interval Level Data
• Pearson's r
• Conclusion: Selecting the Most Appropriate Measure of Strength
• Chapter Resources

10. Measures of Direction and Nature of a Relationship
• Direction of the Association
- Establishing Direction for Ordinal Level Data
- Establishing Direction for Interval and Ratio Level Data
• Nature of Association
- Establishing the Nature of the Distribution for Nominal Level Data
- Nature of Association
- Establishing the Nature of the Distribution for Interval and Ratio Level Data
• Conclusion
• Chapter resources

11. Introduction to Multivariate Statistics
• When Two Variables Just Aren't Enough
• Interaction Among Variables
• Causation
- Association
- Temporal Ordering
- Elimination of Confounding Variables
• Additional Concepts in Multivariate Analysis
- Robustness
- Error
- Parsimony
• Conclusion
• Chapter resources

12. Multiple Regression I: Ordinary Least Squares Regression
• Regression
• Assumptions
• Analysis and Interpretation
- Steps in OLS Regression Analysis
- Other OLS Regression Analysis
- Limitations of OLS Regression
• Multicollinearity
- Assessing Multicollinearity
- Adjusting for Multicollinearity
• Conclusion
• Chapter resources

13. Multiple Regression II: Limited Dependent Variables
• Dealing with Limited Dependent Variables
- OLS Assumptions That Are Violated by Dichotomous Variables
• Assumptions and Use of Logistic Regression
- Interpreting Logit Results
- Interactive Effects and Other Types of Logit
- Criticisms of Logistic Regression
• Poisson and Negative Binomial Regression
- Interpreting Poisson and Negative Binomial Regression
- Criticisms of Poisson and Negative Binomial Models
• Other Regression Procedures
- Probit Regression
- Tobit Regression
• Conclusion
• Chapter Resources

14. Introduction to Inferential Analysis
• Terminology and Assumptions
• Normal Curve
• Probability
• Sampling
- Probability Sampling
- Nonprobability Sampling
• Sampling Distributions
• Central Limit Theorem
• Confidence Levels
- Calculating Confidence Intervals
- Interpreting Confidence Intervals
• Conclusion
• Chapter Resources

15. Hypothesis Testing
• Null and Research Hypotheses
• Steps in Hypothesis Testing
• Type I and Type II Errors
- Which is Better, Type I or Type II Error?
• Power of Tests
• Conclusion
• Chapter Resources

16. Hypothesis Tests
• Z-Test
- Calculation and Example
- Interpretation and Application: Known Probability of Error
- One- versus Two-Sample Z-tests
• t-test
- Assumptions of a t-test
- Calculation and Example
- SPSS Analysis for Z-tests and t-tests
• Chi-square Test for Independence
• Conclusion
• Chapter Resources

17. Analysis of Variance (ANOVA)
- Assumptions
- Calculation and Interpretation
• Post Hoc Tests
• Conclusion
• Chapter Resources

18. Putting It All Together
• The Relationship Between Statistics, Methodology, and Theory
• Describe It or Make Inferences
• Abuse of Statistics
• When You Are On Your Own
• Conclusion
• Chapter Resources


Jeffery T. Walker, Ph.D. - University of Arkansas
Jeffery T. Walker is a Professor and the Graduate Coordinator of Criminal Justice at the University of Arkansas-Little Rock, and is the president-elect of the Academy of Criminal Justice Sciences (ACJS).

Sean Maddan, Ph.D. - Texas Christian University
Sean Maddan is a lecturer in the department of Sociology, Criminal Justice, and Anthropology at Texas Christian University.

237 pages

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