Advanced Statistics in Criminology and Criminal Justice / by David Weisburd, David B. Wilson, Alese Wooditch, Chester Britt
| Publisher | (Cham : Springer International Publishing : Imprint: Springer) |
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| Year | 2022 |
| Edition | 5th ed. 2022. |
| Authors | *Weisburd, David author Wilson, David B author Wooditch, Alese author Britt, Chester author SpringerLink (Online service) |
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| Links to the text | Location | Volume | Call No. | Barcode No. | Status | Comments | ISBN | Printed | Restriction | Reserve |
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| Links to the text | Library Off-campus access |
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OB00176943 | Springer Law and Criminology eBooks (電子ブック) | 9783030677381 |
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| Material Type | E-Book |
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| Media type | 機械可読データファイル |
| Size | IX, 550 p. 68 illus., 10 illus. in color : online resource |
| Notes | Chapter 1. Introduction -- Chapter 2. Multiple Regression- Chapter 3. Multiple Regression: Additional Topics -- Chapter 4. Logistic Regression -- Chapter 5. Multivariate Regression With Multiple Category Nominal or Ordinal Measures -- Chapter 6. Count-Based Regression Models -- Chapter 7. Multilevel Regression Models -- Chapter 8. Statistical Power -- Chapter 9. Special Topics: Randomized Experiments -- Chapter 10. Propensity Score Matching -- Chapter 11. Meta-Analysis -- Chapter 12. Spatial Regression 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 HTTP:URL=https://doi.org/10.1007/978-3-030-67738-1 |
| Subjects | LCSH:Criminology LCSH:Social sciences—Statistical methods FREE:Criminology FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy |
| Classification | LCC:HV6001-7220.5 DC23:364 |
| ID | 8000079052 |
| ISBN | 9783030677381 |
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