Introductory Statistics for Criminal Justice

The science of criminal justice has seen expansive growth over the past decade, and new standards have been set by the Academy of Criminal Justice Sciences. This book makes statistical concepts digestible using Microsoft Excel, the software most likely to be used in the workplace, versus more sophisticated packages.Focuses on basic techniques, computations, and practical applications rather than on technical explanations and overly advanced techniques. Offers real and hypothetical examples throughout. Presents in each chapter a problem and solution that introduces the topic to be discussed, and defines and illustrates the relevant statistical procedures.An accessible reference for novices as well as criminal justice practitioners.

Jon L. Proctor received his bachelor's and master's degrees in criminal justice from the University of Nebraska and his doctorate in sociology from the University of Nebraska. His teaching interests are primarily in the areas of research methods, statistics, and corrections-related courses. His research focuses on a variety of correctional issues, including classification, parole, and institutional control of inmate populations.

Diane M. Badzinski received her bachelor's degree from St. Cloud State University, her master's degree from the University of California, Santa Barbara, and her doctorate from the University of Wisconsin-Madison. After ten years of teaching at the University of Nebraska-Lincoln, she resigned her position and now teaches part-time. Although her teaching interests and responsibilities often lie in research methods and statistics, she also enjoys teaching a variety of communication courses.

1. Purpose of Statistics.
2. Foundations of Research.
3. Data Organization.
4. Measures of Central Tendency.
5. Measures of Dispersion.
6. Probability Theory.
7. Sample Statistics to Population Parameters.
8. Statistical Analysis for a Population Mean and Proportion: z Tests.
9. Statistical Analysis for Nominal and Ordinal Variables: Chi-Square and Spearman's Rho.
10. Statistical Analysis for Comparing Two Population Means: t Tests.
11. Statistical Analysis for Comparing Three or More Population Means: Analysis of Variance.
12. Statistical Analysis for Assessing Relationships: Correlation.
13. Statistical Analysis for Prediction: Regression.
Appendix A. Probability Distribution Tables.
Appendix B. Chapter Solutions for Odd Questions.
Index.

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Introductory Statistics for Criminal Justice and Criminology

Summary

Author Biography

Table of Contents

Supplemental Materials