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Advanced statistics in criminology and criminal justice

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced statistics in criminology and criminal justiceby David Weisburd ... [et al.].
other author:	Weisburd, David.
Published:	Cham :Springer International Publishing :2022.
Description:	ix, 550 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Criminal statistics.
Online resource:	https://doi.org/10.1007/978-3-030-67738-1
ISBN:	9783030677381$q(electronic bk.)

Advanced statistics in criminology and criminal justice
Advanced statistics in criminology and criminal justice[electronic resource] /by David Weisburd ... [et al.]. - Fifth edition. - Cham :Springer International Publishing :2022. - ix, 550 p. :ill. (some col.), digital ;24 cm.

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.

ISBN: 9783030677381$q(electronic bk.)

Standard No.: 10.1007/978-3-030-67738-1doiSubjects--Topical Terms:

329221
Criminal statistics.

LC Class. No.: HV7415

Dewey Class. No.: 364.021

Advanced statistics in criminology and criminal justice
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245 0 0 $a Advanced statistics in criminology and criminal justice $h [electronic resource] / $c by David Weisburd ... [et al.].
250 $a Fifth edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a ix, 550 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $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.
650 0 $a Criminal statistics. $3 329221
650 1 4 $a Criminology and Criminal Justice, general. $3 758192
650 2 4 $a Statistics for Social Sciences, Humanities, Law. $3 825904
700 1 $a Weisburd, David. $3 261207
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-67738-1
950 $a Law and Criminology (SpringerNature-41177)