Diversifying Crime Datasets in Introductory Statistical Courses in Criminology

Abstract

Contemporary criminology issues are increasingly global, cross-cultural, and multilingual. Moreover, students from different cultural and national backgrounds will need to apply data analytics in their respective contexts. Crime data used in statistical courses should reflect this diversity, and in turn enhance the equality and inclusivity of the teaching curriculum. Supported by evidence-based pedagogic principles and evaluations, researchers have identified strategies to enhance the teaching and learning of quantitative skills. Promoting students’ understanding of quantitative methods and their application in criminology requires that teaching materials reflect real-world problems and the diversity of today’s student population. To facilitate this aim, the article first describes over 40 open and accessible crime data sources across political, cultural, and linguistic borders in the Global South. Moreover, to support educators in their implementation and use of these datasets, the article presents three case studies of exemplar pedagogic activities using available data sources in an undergraduate Criminology program in the UK. Exemplar activities include (1) time series analysis of homicide in Asia; (2) bivariate analysis of trust in police and victimization in Algeria; and (3) mapping kidnappings in Mexico. We end by discussing the pedagogical and research implications of diversifying datasets and some future challenges.