Alese Wooditch – författare

This introductory textbook takes a building-block approach that emphasizes the application and interpretation of statistics in research in crime and justice. This text is meant for both students and professionals who want to gain a basic understanding of common statistical methods used in criminology and criminal justice before advancing to more complex statistical analyses in future volumes. 

Basic Statistics in Criminology and Criminal Justice aims to give students and researchers a core understanding of statistical concepts and methods that will leave them with the confidence and tools to tackle the statistical problems in their own research work.

This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages.

The text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as Basic Statistics in Criminal Justice (2020).

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 designstudies. 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.