CRIME PREVENTION|EVALUATION|CRIME REDUCTION MEASURES|EVIDENCE|CRIME PATTERN ANALYSIS

This module equips students with the knowledge and skills needed effectively to evaluate crime reduction measures. Students will be introduced to evaluation...

Evaluation of Crime Prevention Measures (SECU0052)

This module equips students with the knowledge and skills needed effectively to evaluate crime reduction measures. Students will be introduced to evaluation theory, learn how theories can be applied in practice, and design their own process and impact evaluation plan. Finally, they will examine the concept of evidence and be taught techniques to appraise and synthesise evidence reported in multiple evaluations of a single intervention, as is undertaken in systematic reviews. Complementing earlier modules in the BSc programme, this module draws upon knowledge from various areas, including systems theory, crime pattern analysis, crime prevention, and research methodology. The module will be delivered over 10 weeks with 3 hours of contact time (lectures/tutorials) per week.

This is the dissertation module and is compulsory for all MSc students in the Department of Security and Crime Science. Students submit a dissertation on a...

Security and Crime Science Dissertation (SECU0045)

This is the dissertation module and is compulsory for all MSc students in the Department of Security and Crime Science. Students submit a dissertation on a piece of research no longer than 10,000 words.

quantitative methods|statistical inference|applied regression analysis

This module equips students with the knowledge needed to understand and critically evaluate crime and security research, as well as the skills required to...

Probability, Statistics and Modelling II (SECU0013)

This module equips students with the knowledge needed to understand and critically evaluate crime and security research, as well as the skills required to analyse and learn from data. Building on the PSM I module, students get to know the generalised linear model with various link functions, including linear, binary logistic, multinomial logistic, ordinal logistic, and count models. You can only learn statistics by doing statistics. This means that this course will focus on statistical inference, intuition, and interpretation. It will also teach students how to use the statistical programme R for statistical modelling. This module consists of main lectures, supplementary lectures, and tutorials. The main lectures cover statistical concepts and show what they mean and how to apply them to research questions. The tutorials help you put these concepts into practice using the statistical software R. Finally, the supplementary lectures provide deep dives into topics that are pertinent to applied data analysis (such as how to analyse surveys or missing data) and ethical research practices (such as “p-hacking” and pre-registration).