| CODE | CRI1033 | ||||||||
| TITLE | Quantitative Research Methods for Criminology | ||||||||
| UM LEVEL | 01 - Year 1 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 5 | ||||||||
| ECTS CREDITS | 4 | ||||||||
| DEPARTMENT | Criminology | ||||||||
| DESCRIPTION | The study-unit centres around the necessity of thoroughly analysing and interpreting crime data to draw meaningful conclusions. It aims to equip students with the skills and knowledge to transform raw data into actionable insights through analytical methods, data exploration, and statistical analysis. Starting with the principles of data analysis, students will explore foundational theories, research question framing, and variable definition. The study-unit introduces statistical methods used in criminology, including descriptive statistics, regression analysis, and hypothesis testing, teaching students to select appropriate techniques for various data types and research questions. Focus is then placed on data interpretation, where students develop skills to critically evaluate and draw conclusions from statistical results, effectively communicating findings to stakeholders. Practical training in statistical software provides hands-on experience in data manipulation, analysis, and visualisation, essential for modern criminological research. Study-Unit Aims: - To provide an insight into the relevance of sound statistical analysis in criminology; - To enable students to select appropriate methods for data analysis and interpretation; - To familiarise students with data exploration techniques and statistical software; - To make students aware of the elements of effective data analysis and interpretation; - To enable students to develop skills in data-driven decision-making within criminology. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Recognise the principles underlying effective criminological data analysis; - Identify appropriate strategies for data exploration and statistical analysis; - Explain how to critically analyse and interpret crime data; - Demonstrate the use of statistical methods in criminology; - Produce meaningful interpretations of criminological data. 2. Skills: By the end of the study-unit the student will be able to: - Apply the principles of effective criminological data analysis; - Implement appropriate strategies for data exploration and statistical analysis; - Conduct critical analysis and interpretation of crime data; - Utilise statistical methods in practical criminology scenarios; - Produce and present meaningful interpretations of criminological data in various formats. Main Text/s and any supplementary readings: Main Texts: - Alberti, G. (2024). From Data to Insights: A Beginner's Guide to Cross-Tabulation Analysis. Chapman & Hall. - Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten (Second ed.). Analytics Press. - Gau, J. M. (2019). Statistics for criminology and criminal justice (3rd ed.). SAGE Publications. - Healy, K. (2019). Data Visualization. A Practical Introduction (1st ed.). Princeton University Press. - Kirk, A. (2019). Data Visualisation: A Handbook for Data Driven Design (2nd ed.). SAGE Publications Ltd. - Rahlf, T. (2020). Data Visualisation with R: 111 Examples (2nd ed. 2019 ed.). Springer. - Rowntree, D. (2018). Statistics without Tears: An Introduction for Non-Mathematicians. Penguin UK. - Swires-Hennessy, E. (2014). Presenting Data: How to Communicate Your Message Effectively (1st ed.). Wiley. - Wilke, C. O. (2019). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures (1st ed.). O鈥橰eilly Media. - Wooditch, A., Johnson, N. J., Solymosi, R., Medina Ariza, J., & Langton, S. (2021). A Beginner鈥檚 Guide to Statistics for Criminology and Criminal Justice Using R. Springer Nature Switzerland AG. Supplementary Readings: - Chang, W. (2018). R Graphics Cookbook: Practical Recipes for Visualizing Data (2nd ed.). O鈥橰eilly Media. - Dick, M. (2020). The Infographic: A History of Data Graphics in News and Communications (History and Foundations of 福利在线免费 Science). The MIT Press. - Spatz, C. (2019). Exploring Statistics. Macmillan Publishers. - Zumel, N., & Mount, J. (2019). Practical Data Science with R (2nd ed.). Manning. |
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| STUDY-UNIT TYPE | Lecture | ||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Gianmarco Alberti |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
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