PGDip Criminology

Networks are becoming increasingly prominent in both criminological research and its application in practice. This unique course provides an opportunity to learn the most important methods from social network analysis in a criminological context with hands-on approach to data collection and analysis while introducing students to the current debates and issues in the field. 

CRIM71502 programme requirement

The aim of the course unit is to introduce students to social network analysis in the context of criminology and its applications there. The first part of the course focuses on data sources, how to use them to construct network data, and the opportunities and risks each data source entails. Subsequently, the course continues with substantive topics starting with criminal enterprise networks, then goes through terrorist networks to population-level co-offending networks. With each substantive area, the frequently used methods and measures are contextualised and demonstrated.

Syllabus (indicative curriculum content): 

• network analysis and network perspective in criminology, relational aspects of crime;
• data collection 1 - content analysis of archival data, survey design for ego-networks, electronic transcripts of transactions and interactions;
• data collection 2 - validity, reliability, and accessibility issues; problem of missing data, ethics;
• organised crime from network perspective 1 - central actors in trafficking, terrorist, and corruption networks and their importance;
• organised crime from network perspective 2 - structure and dynamics and their underlying mechanisms;
• co-offending and networks - specific empirical phenomenon à specific theories and analytical approaches;
• inter-gang relations and neighbourhood level crime - patterns of conflict, contagion of violence;
• ego-networks and deviance - personal networks perspective and analysis related to mainstream criminological theories of crime and deviance (self-control, situational action etc.);
  

Besides the ILOs, students will also get the following additional non-assessed benefits:

• Students will become familiar with a relatively new scientific subfield with all the opportunities and drawbacks in building and critically evaluating theories, applying methods, and cumulating evidence;
• Upon completing the course, students will have a strong basis of knowledge about the most important theories, methods, and findings in criminal network analysis as well as their application in devising evidence-based prevention and intervention measures;
• Students will improve their oral presentation skills and ability to prepare a well-designed and easy to follow presentation, which is crucial in the job market.

In the practical part of the course, students will work with freely available programming language R. Thus, they will learn the basics of writing and understanding computer code and principles of programming.

Furthermore, social network analysis itself is an integral part in data literacy and understanding complexity, so students will also improve their digital literacy while mastering the main part of the course. 

Description of T&L Methods

- lecture: lecture with interactive elements (discussion, brainstorming) – 1 hour/week

- seminar exercises: group discussion, group collaboration, presentation – 1 hour/week

- computer-based exercise: data preparation and analysis - 1 hour/week

- self-study: background reading –  15 hours per week

Data analysis (using the collected data to conduct a basic yet thorough social network analysis with interpretation of results) and its oral presentation using pre-prepared slides. The students will do so in groups.

Bichler, G., & Malm, A. (2015). Disrupting Criminal Networks. Lynne Rienner Publishers.

Bichler, G., Malm, A., & Cooper, T. (2017). Drug supply networks: A systematic review of the organizational structure of illicit drug trade. Crime Science, 6(1).

Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing Social Networks. SAGE publications.

Cunningham, D., Everton, S., & Murphy, P. (2016). Understanding Dark Networks: A Strategic Framework for the Use of Social Network Analysis. Rowman & Littlefield Publishers.

Diviák, T. (2018). Sinister connections: How to analyse organised crime with social network analysis? AUC PHILOSOPHICA ET HISTORICA, 2018(2), 115–135.

Faust, K., & Tita, G. E. (2019). Social Networks and Crime: Pitfalls and Promises for Advancing the Field. Annual Review of Criminology, 2(1), 99–122.

Morselli, C. (2009). Inside Criminal Networks. New York, NY: Springer New York.

Morselli, C. (2014). Crime and Networks. New York: Routledge.

Robins, G. (2015). Doing Social Network Research. SAGE publications.

Valente, T. W. (2012). Network Interventions. Science, 337(6090), 49–53.

von Lampe, K. (2016). Organized Crime: Analyzing Illegal Activities, Criminal Structures, and Extra-legal Governance (1 edition). SAGE Publications, Inc.

Only available to students on the following programmes;

• MA Criminology;
• MRes Criminology;
• MRes Criminology with Social Statistics;