Advanced statistics in criminology and criminal justice
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. Multilev...
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| Otros Autores: | ; ; |
| Tipo de documento: | Electronic/Print Libro |
| Lenguaje: | Inglés |
| Publicado: |
Cham
Springer
[2022]
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| En: | Año: 2022 |
| Edición: | Fifth edition |
| Acceso en línea: |
Índice Texto de la solapa Presumably Free Access Volltext (doi) |
| Disponibilidad en Tübingen: | Disponible en Tübingen. UB: KB 21 A 2087 |
| Verificar disponibilidad: | HBZ Gateway |
| Palabras clave: | |
| Publicación relacionada: | Erscheint auch als:
1775332527 |
MARC
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| 109 | |a Weisburd, David 1954- |a Weisburd, David L. 1954- |a Weisburd, David Lee 1954- | ||
| 240 | 1 | 0 | |a Statistics in criminal justice |
| 245 | 1 | 0 | |a Advanced statistics in criminology and criminal justice |c David Weisburd (Department of Criminology, Law and Society, George Mason University, Fairfax, VA, USA and Institute of Criminology, Faculty of Law, Hebrew University of Jerusalem, Jerusalem, Israel), David B. Wilson (Department of Criminology, Law and Society, George Mason University, Fairfax, VA, USA), Alese Wooditch (Department of Criminal Justice, Temple University, Philadelphia, PA, USA), and Chester Britt (Department of Sociology, Iowa State University, Ames, IA, USA) |
| 250 | |a Fifth edition | ||
| 264 | 1 | |a Cham |b Springer |c [2022] | |
| 300 | |a ix, 550 Seiten |b Illustrationen |c 26 cm | ||
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| 500 | |a Includes bibliographical references and index | ||
| 520 | |a 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. | ||
| 520 | |a "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."--Back cover | ||
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