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Data Analysis for Criminal Justice : Practice and Applications

Data Analysis for Criminal Justice : Practice and Applications - 00 edition

ISBN13: 978-0205274802

Cover of Data Analysis for Criminal Justice : Practice and Applications 00 (ISBN 978-0205274802)
ISBN13: 978-0205274802
ISBN10: 0205274803
Cover type:
Edition/Copyright: 00
Publisher: Allyn & Bacon, Inc.
Published: 2000
International: No

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Data Analysis for Criminal Justice : Practice and Applications - 00 edition

ISBN13: 978-0205274802

Jerome B. McKean and Bryan D. Byers

ISBN13: 978-0205274802
ISBN10: 0205274803
Cover type:
Edition/Copyright: 00
Publisher: Allyn & Bacon, Inc.

Published: 2000
International: No
Summary

Unique to the market, this user-friendly book offers a comprehensive introduction to data analysis in criminal justice and criminology. Ideal for readers with a limited math background, Data Analysis in Criminal Justice and Criminology offers a clear presentation of data analytic methodologies combined with examples, research, and exercises that foster active learning. Study questions and exercises throughout encourage readers to apply data analytic knowledge and skills, serving to engage student interest while preparing them for careers as practitioners. In addition, real-world examples of research studies show practical applications of text material, helping students to understand the relevance of statistics in criminal justice and criminology.

Author Bio

McKean, Jerome B. : Ball State University


Byers, Bryan D. : Ball State University

Table of Contents

1. An Introduction to Descriptive and Inferential Statistics in Criminal Justice.

Levels of Measurement.
Descriptive Statistics.
Ratios, Percentages, Proportions, and Rates.
Measures of Central Tendency.
Measures of Dispersion.
Introducing Statistical Concepts.
Tracy L. Snell, Capital Punishment 1996.

2. Constructing and Interpreting Contingency Tables.

Cross-Tabulation.
Cross-Tabulation with Ordinal Variables.
Multivariate Analysis of Contingency Tables.
Byron R. Johnson, David B. Larson, and Timothy C. Pitts, Religious Programs, Institutional Adjustment, and Recidivism among Former Inmates in Prison Fellowship Programs.

3. Testing Hypotheses with Contingency Tables: Chi-Square.

Relationships in Populations and Samples.
The Chi-Square Test.
Interpretation of Chi-Square.
The Meaning of Statistical Significance.
Michael G. Breci, Female Officers on Patrol: Public Perceptions in the 1990s.

4. Measures of Association Used with Contingency Tables.

An Introduction to Measures of Association.
Measures of Association Used with 2 x 2 Tables.
Interpreting Measures of Association.
Association Measures Used with R x C Tables.
Association Measures and the Elaboration Paradigm.
James J. Sobol, Behavioral Characteristics and Levels of Involvement for Victims of Homicide.

5. The Normal Distribution and Confidence Intervals.

The Normal Distribution.
Population Parameters and Sample Statistics.
The Central Limit Theorem.
Confidence Intervals and Confidence Levels.
Confidence Intervals for Proportions and Percentages.
Michael D. Maltz and Marianne W. Zavitz, Displaying Violent Crime Trends Using Estimates from the National Crime Victimization Survey.

6. Comparing Two Sample Means.

Null and Alternative Hypotheses.
The Sampling Distribution.
Type I Error.
Computing the Test Statistic.
Decision Making.
Significance Tests and Confidence Intervals.
Ann G. Crocker and Shielagh Hodgins, The Criminality of Noninstitutionalized Mentally Retarded Persons: Evidence from a Birth Cohart to Age 30.

7. Analysis of Variance.

An Introduction to Analysis of Variance.
Elements of the ANOVA Procedure.
One-Way ANOVA, Two-Way ANOVA and MANOVA.
Step-by-Step One-Way ANOVA Example.
Sara R. Battin, Karl G. Hill, Robert D. Abbott, Richard F. Catalono, and J. David Hawkins, The Contribution of Gang Membership to Delinquency beyond Delinquent Friends.

8. Correlation and Simple Regression.

Scatterplots.
Linear Relationships.
Estimating the Linear Regression Equation.
The Correlation Coefficient (r) and the Coefficient of Determination (r^ 2).
The Correlation Matrix.
A Final Note on Correlation and Regression.

9. Multiple Regression and Correlation.

Introduction to Multivariate Regression Analysis.
Model Specification in Multiple Regression.
Partial Regression Slope.
Multiple Regression Equation.
Partial Correlation.
Standardized Multiple Regression Equation.
Multiple Correlation and the Coefficient of Multiple Determination, r^ 2.
Coefficient of Alienation.
Coefficient of Multiple Correlation, r.
The t and F Tests.
Interpreting Multiple Regression.
Liqun Cao, James Frank, and Francis Cullen, Race, Community Context, and Confidence in the Police.

Appendix A: Criminal Justice Research Internet Site Guide.
Appendix B: Statistical Tables.

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