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Advanced Statistics in Criminology and Criminal Justice Fifth Edition 2022 [Kõva köide]

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  • Formaat: Hardback, 550 pages, kõrgus x laius: 254x178 mm, kaal: 1237 g, 10 Illustrations, color; 58 Illustrations, black and white; IX, 550 p. 68 illus., 10 illus. in color., 1 Hardback
  • Ilmumisaeg: 22-Oct-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030677370
  • ISBN-13: 9783030677374
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Advanced Statistics in Criminology and Criminal Justice Fifth Edition 2022Suurem pilt
  • Formaat: Hardback, 550 pages, kõrgus x laius: 254x178 mm, kaal: 1237 g, 10 Illustrations, color; 58 Illustrations, black and white; IX, 550 p. 68 illus., 10 illus. in color., 1 Hardback
  • Ilmumisaeg: 22-Oct-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030677370
  • ISBN-13: 9783030677374
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030677374.html

This book provides the student, researcher or practitioner with the tools to understand many of the most commonly used advanced statistical analysis tools in criminology and criminal justice, and also to apply them to research problems.  

The volume is structured around two main topics, giving the user flexibility to find what they need quickly. The first is “the general linear model” which is the main analytic approach used to understand what influences outcomes in crime and justice.  It presents a series of approaches from OLS multivariate regression, through logistic regression and multi-nomial regression, hierarchical regression, to count regression. The volume also examines alternative methods for estimating unbiased outcomes that are becoming more common in criminology and criminal justice, including analyses of randomized experiments and propensity score matching. It also examines the problem of statistical power, and how it can be used to better design studies. Finally, it discusses meta analysis, which is used to summarize studies; and geographic statistical analysis, which allows us to take into account the ways in which geographies may influence our statistical conclusions.


Chapter One Introduction
1(14)
Proportionality Review and the Supreme Court of New Jersey: A Cautionary Tale
3(4)
Generalized Linear Models
7(6)
Special Topics
13(1)
References
14(1)
Chapter Two Multiple Regression
15(58)
Overview of Simple Regression
17(6)
Extending Simple Regression to Multiple Regression
23(4)
Assumptions of Multiple Regression
27(5)
Measurement Error in the Independent Variables
32(1)
Regression Diagnostics
33(5)
Dealing with Outliers and Influential Cases
38(2)
Testing the Significance of Individual Regression Coefficients
40(1)
Assessing Overall Model Fit and Comparing Nested Models
41(5)
Comparing Regression Coefficients Within a Single Model: The Standardized Regression Coefficient
46(2)
Correctly Specifying the Regression Model
48(2)
Model Specification and Building
50(3)
An Example of a Multiple Regression Model
53(6)
Chapter Summary
59(1)
Key Terms
60(1)
Symbols and Formulas
61(2)
Exercises
63(3)
Computer Exercises
66(6)
References
72(1)
Chapter Three Multiple Regression: Additional Topics
73(54)
Nominal Variables with Three or More Categories in Multiple Regression
76(4)
Nonlinear Relationships
80(12)
Interaction Effects
92(4)
An Example: Race and Punishment Severity
96(9)
An Example: Punishment Severity
105(4)
The Problem of Multicollinearity
109(3)
Chapter Summary
112(1)
Key Terms
113(1)
Symbols and Formulas
113(1)
Exercises
114(4)
Computer Exercises
118(8)
References
126(1)
Chapter Four Logistic Regression
127(60)
Why Is It Inappropriate to Use OLS Regression for a Dichotomous Dependent Variable?
130(6)
Logistic Regression
136(10)
A Substantive Example: Adoption of Compstat in U.S. Police Agencies
146(5)
Interpreting Logistic Regression Coefficients
151(7)
Comparing Logistic Regression Coefficients
158(8)
Evaluating the Logistic Regression Model
166(3)
Statistical Significance in Logistic Regression
169(4)
Chapter Summary
173(2)
Key Terms
175(1)
Symbols and Formulas
176(2)
Exercises
178(3)
Computer Exercises
181(4)
References
185(2)
Chapter Five Multiple Regression with Multiple Category Nominal or Ordinal Measures
187(46)
Multinomial Logistic Regression
190(15)
Ordinal Logistic Regression
205(14)
Chapter Summary
219(1)
Key Terms
220(1)
Formulas
221(1)
Exercises
222(3)
Computer Exercises
225(6)
References
231(2)
Chapter Six Count-Based Regression Models
233(40)
The Poisson Distribution
236(3)
Poisson Regression
239(10)
Over-Dispersion in Count Data
249(2)
Quasi-Poisson and Negative Binomial Regression
251(4)
Zero-Inflated Poisson and Negative Binomial Regression
255(4)
Chapter Summary
259(1)
Key Terms
260(1)
Symbols and Formulas
261(1)
Exercises
262(1)
Computer Exercises
263(8)
References
271(2)
Chapter Seven Multilevel Regression Models
273(48)
A Simple Multilevel Model
277(10)
Random Intercept Model with Fixed Slopes
287(8)
Random Coefficient Model
295(5)
Adding Cluster (Level 2) Characteristics
300(9)
Chapter Summary
309(1)
Key Terms
310(1)
Symbols and Formulas
311(1)
Exercises
312(3)
Computer Exercises
315(4)
References
319(2)
Chapter Eight Statistical Power
321(46)
Statistical Power
323(3)
Components of Statistical Power
326(9)
Estimating Statistical Power and Sample Size for a Statistically Powerful Study
335(11)
Summing Up: Avoiding Studies Designed for Failure
346(1)
Chapter Summary
347(1)
Key Terms
348(1)
Symbols and Formulas
348(1)
Computer Exercises
349(16)
References
365(2)
Chapter Nine Randomized Experiments
367(50)
The Structure of a Randomized Experiment
368(3)
The Main Advantage of Experiments: Isolating Causal Effects
371(4)
Internal Validity
375(2)
Selected Design Types and Associated Statistical Methods
377(12)
Block Randomized Designs
389(11)
Using Covariates to Increase Statistical Power in Experimental Studies
400(2)
Chapter Summary
402(1)
Key Terms
403(1)
Symbols and Formulas
404(4)
Exercises
408(1)
Computer Exercises
409(6)
References
415(2)
Chapter Ten Propensity Score Matching
417(34)
The Underlying Logic Behind Propensity Score Matching
419(2)
Selection of Model for Predicting Propensity for Treatment
421(1)
Matching Methods
422(5)
Assessing the Quality of the Matches
427(4)
Sensitivity Analysis for Average Treatment Effects
431(2)
Limitations of Propensity Score Matching
433(2)
Chapter Summary
435(1)
Key Terms
436(1)
Symbols and Formulas
437(1)
Exercises
437(1)
Computer Exercises
438(10)
References
448(3)
Chapter Eleven Meta-analysis
451(48)
A Historical Note
454(1)
The Logic of Meta-analysis
455(1)
The Effect Size
456(11)
Meta-analysis of Effect Sizes
467(7)
Forest Plots
474(1)
Moderator Analysis
475(5)
Handling Statistically Dependent Effect Sizes: Robust Standard Errors
480(2)
Publication Selection Bias
482(3)
Chapter Summary
485(1)
Key Terms
486(1)
Symbols and Formulas
486(4)
Exercises
490(1)
Computer Exercises
491(5)
References
496(3)
Chapter Twelve Spatial Regression
499(38)
Why Can't We Use OLS Regression with Spatial Data?
501(1)
How Do We Define Spatial Relationships?
502(8)
What Is Spatial Regression?
510(4)
Which Type of Spatial Regression Should I Use?
514(4)
Spatial Regression Example
518(5)
Chapter Summary
523(1)
Key Terms
524(1)
Symbols and Formulas
525(1)
Exercises
526(2)
Computer Exercises
528(7)
References
535(2)
Glossary 537(6)
Index 543
David Weisburd is a leading researcher and scholar in criminology and criminal justice. He is Distinguished Professor of Criminology, Law and Society at George Mason University in Virginia and Walter E. Meyer Professor of Law and Criminal Justice at the Hebrew University of Jerusalem. Professor Weisburd has received many awards and prizes for his contributions to criminology and criminal justice including the Stockholm Prize in Criminology and the Sutherland and Vollmer Awards from the American Society of Criminology. 





Chester Britt was a leading researcher and scholar in the field of criminology. During his career, he taught at a number of universities and led departments at Northeastern University, Arizona State University, and the University of Iowa. His research addressed theories of criminal behavior and victimization, demography of crime and criminal careers, criminal justice decision-making, and quantitative research methods.





David Wilson is a Professor in the Criminology, Law and Society Department at George Mason University in Virginia. He is a leading statistician in the field of criminology and was the recipient of the Mosteller Award from the Campbell Collaboration for his contributions to the science of systematic review and meta-analysis.





 Alese Wooditch is an Assistant Professor of Criminal Justice at Temple University. She received her PhD from George Mason University. Professor Wooditch has worked in the area of innovative spatial statistical analyses and has contributed to a number of research programs focusing on crime prevention and Agent-Based Modeling.