|Edition description:||Softcover reprint of the original 4th ed. 2014|
|Product dimensions:||7.01(w) x 10.00(h) x (d)|
About the Author
David Weisburd (Ph.D., Yale University) is a leading researcher and scholar in the field of criminal justice. He is Professor of Criminology at the Hebrew University Law School in Jerusalem and is a professor in the Department of Criminology and Criminal Justice at the University of Maryland. Professor Weisburd serves as a senior fellow at the Police Foundation in Washington DC, and is a member of the National Academy of Sciences Panel on Police Practices and Polices and the steering committee of the Campbell Crime and Justice Coordinating Group.
Chester Britt (Ph.D, University of Arizona) is a researcher and scholar in the field of criminology. He is Associate Professor in the Administration of Justice Department at Arizona State University West. Professor Britt is the editor for Justice Quarterly. He has published more than twenty scientific articles and book chapters on issues related to the demography of crime, criminal careers, criminal case processing, and statistics.
Table of Contents1. Introduction: Statistics as a Research Tool. 2. Measurement: The Basic Building Block of Research. 3. Describing the Typical Case: Measures of Central Tendency. 4. How Typical Is the Typical Case: Measuring Dispersion. 5. Representing an Array of Data: Frequency Distributions. 6. The Logic of Statistical Inference: Making Statements About Populations on the Basis of Sample Statistics. 7. Making Sense of Significance: Going from Statistical Probabilities to a Sampling Distribution. 8. Steps in a Statistical Test: Using the Binomial Distribution to Make Decisions About Hypotheses. 9. Chi Square: A Commonly Used Test for Normal Level Data. 10. Parametric Tests for Interval Level Variables: Single Sample Tests. 11. Parametric Tests for Interval Level Variables: The Two-Sample Case. 12. Comparing Means Among Multiple Samples: Analysis of Variance. 13. Statistical Power: Avoiding Studies That Are Designed for Failure. 14. Correlation: Measuring How Well Variables Are Related. 15. Bivariate Regression: Moving from Correlation to Causality. 16. Multivariate Regression: Controlling and Comparing Multiple Causes.