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David Weisburd - Advanced Statistics in Criminology and Criminal Justice

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David Weisburd Advanced Statistics in Criminology and Criminal Justice

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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.

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Book cover of Advanced Statistics in Criminology and Criminal Justice David - photo 1
Book cover of Advanced Statistics in Criminology and Criminal Justice
David Weisburd , David B. Wilson , Alese Wooditch and Chester Britt
Advanced Statistics in Criminology and Criminal Justice
5th ed. 2022
Logo of the publisher David Weisburd Department of Criminology Law and - photo 2
Logo of the publisher
David Weisburd
Department of Criminology, Law and Society, George Mason University, Fairfax, VA, USA
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
Chester Britt
Department of Sociology, Iowa State University, Ames, IA, USA
ISBN 978-3-030-67737-4 e-ISBN 978-3-030-67738-1
https://doi.org/10.1007/978-3-030-67738-1
Springer Nature Switzerland AG 2022
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AG

The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents
Springer Nature Switzerland AG 2022
D. Weisburd et al. Advanced Statistics in Criminology and Criminal Justice https://doi.org/10.1007/978-3-030-67738-1_1
Introduction
David Weisburd
(1)
Institute of Criminology, Faculty of Law, Hebrew University of Jerusalem, Hebrew University of Jerusalem Inst. of Criminology, Fac. of Law, Inst. of Criminology, Fac. of Law, Fairfax, VA, USA, Jerusalem, Israel
(2)
Department of Criminology, Law and Society, George Mason University, Manassas, VA, USA
(3)
Department of Criminal Justice, Temple University, Philadelphia, PA, USA
(4)
Department of Sociology, Iowa State University Department of Sociology, Ames, IA, USA
David Weisburd (Corresponding author)
Email:
David B. Wilson
Email:
Alese Wooditch
Email:
Chester Britt
Email:
Keywords
Advanced statistical techniques Statistical assumptions Death penalty
Proportionality Review and the Supreme Court of New Jersey: A Cautionary Tale
  • What is Proportionality Review?

  • How Were Advanced Statistical Techniques Used in Proportionality Review in New Jersey?

  • What Were the Underlying Problems that Led to Misuse of Advanced Statistical Techniques in this Study?

Generalized Linear Models
  • What is the Generalized Linear Model (GLM)?

  • How can GLM Models be Linear Even Though the Variables Examined are Nonlinear?

  • What Does the Link Function in GLM Do?

  • What is the Importance of the Error Terms in GLM?

  • What are Examples of Different Types of GLMs?

Special Topics
  • What additional statistical additional approaches are examined in the text?

A n advanced volume for classes in statistics is often difficult to define. What are advanced and what are basic statistics? In our Basic Statistics in Criminology and Criminal Justice, we covered materials that would generally be taught in introductory statistics classes at the undergraduate level and first semester Masters level. We dealt with measurement, basic descriptive statistics, and statistical significance. We also laid out the basic approaches for modeling association between variables. While many important studies use these approaches, researchers often encounter problems that need more advanced statistical solutions. Such solutions are the focus of this volume.

As in our Basic Statistics volume, we focus on comprehension and not computation. Nonetheless, the concepts and equations in this volume become more complex as the issues we seek to resolve require innovative statistical solutions. We want to encourage you to do your best to understand the underlying decision making behind these statistical solutions. As we discuss in the next section, more complex statistics can be important in solving problems we encounter in research, but sometimes when statistics become complex, they also can lose transparency and can hide underlying problems with statistical analyses. The better your understanding of such statistics, the better you will be at assessing the quality of research.

We begin this introductory chapter with an example where more complex statistics led to misleading statistical conclusions. It is a cautionary note as we begin our journey to advanced statistical approaches. We then introduce the idea of the generalized linear model (GLM). The GLM is behind most of the statistical methods we introduce in this volume, and we wanted to give the reader a sense at the outset of what GLM is and from where it derives. Regression analyses are key in some form to most of the chapters in our volume. The generalized linear model is the key ingredient of regression analyses , though as we show in later chapters, it can be adapted to many different types of statistical outcomes and problems. In concluding the chapter, we will focus on additional methodological, statistical, and analytic tools that we have included in our volume, discussing briefly why we think they are important for researchers.

Proportionality Review and the Supreme Court of New Jersey: A Cautionary Tale

Can we sometimes go wrong in using advanced statistics ? It is of course always possible that results today will be supplanted by better statistical work later on. And indeed, this is simply part of good science. But sometimes policy makers and even researchers can make the mistake of accepting statistical work as providing valid results when it does not. And this is something we can avoid by understanding the statistical approaches used, and the assumptions that underlie the conclusions that are reached. This is why it is so important for you to not just use advanced statistics but to understand how they are developed and what key assumptions they require.

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