Beginner’s Guide to Statistics for Criminology and Criminal Justice Using R

Alese Wooditch is an assistant professor of criminal justice at Temple University. Her research interests include crime and place, quantitative methods, and evaluation research.Nicole Johnson is a doctoral student in the Department of Criminal Justice at Temple University. Her research interests include understanding group- and community-level behavioral dynamics, particularly those related to crime and responses to crime.Reka Solymosi is a lecturer in quantitative methods at the Department of Criminology at University of Manchester, and a member of the Software Sustainability Institute. Her research focuses on making use of new forms of data to gain insight into people's behaviour and subjective experiences, particularly focusing on crime, victimisation, transport, and spatial research. Juanjo Medina is professor of quantitative criminology and Head of the Criminology Department at the University of Manchester. His research focuses on gender violence, gangs, crime prevention, and data science applications to criminal justice.Samuel Langton is a researcher in quantitative criminology at Manchester Metropolitan University. His research primarily focuses on describing and explaining the spatial and temporal patterning of known offender residences.

• 1. Getting started.- 2. Managing your data.- 3. Data visualization.- 4. Spatiotemporal data visualization and basic crime analysis.- 5. Descriptive statistics: measures of central tendency.- 6. Descriptive statistics: measures of dispersion.- 7. Statistical inference in criminal justice research.- 8. Defining the observed significance level of a test.- 9. Hypothesis testing using the binomial distribution.- 10. Chi-square: a test commonly used for nominal-level measures.- 11. The normal distribution and its application to tests of statistical significance.- 12. Comparing means in two samples.- 13. Analysis of variance.- 14. Measures of association for nominal and ordinal variables.- 15. Measuring association for interval data.- 16. Introduction to regression analysis.