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BASS Politics and Criminology / Course details
Year of entry: 2022
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Course unit details:
Modelling Criminological Data
|Unit level||Level 2|
|Teaching period(s)||Semester 2|
|Offered by||School of Social Sciences|
|Available as a free choice unit?||No|
Data is ubiquitous today and affects all aspects of everyday life. This course aims to provide the student with basic statistical literacy, the ability to understand statistics. In doing so, you will develop a better appreciation of the crime (and many other) stories you read in the media, the arguments made by politicians, and the claims made by scientists. Note, a high mark in this module will render you eligible for paid Q-Step summer internships.
Indicative content: (1) Introduction to the course; (2) Causality in social science; (3) Data visualisation with ggplot2; (4) Data carpentry; (5) Statistical inference; (6) Hypothesis testing; (7) Relationships between categorical variables (8) Regression models; (9) Logistic regression; (10) Course review.
The unit aims to (1) enhance students' understanding of criminological theory in context with particular forms of violence; (2) develop students' awareness of the links between approaches to research, theory construction and policy surrounding violence; (3) explore the complex relationships between power, inequality and violence, drawing upon examples such as ethnicity and gender; (4) examine knowledge and understanding of various forms of violence through critical discussion.
On completion of the course, the student will be able to (1) read and interpret quantitative information in the form of tables and charts; (2) understand basic principles underlying statistical analysis; (3) produce basic descriptive statistics for a dataset; (4) apply statistical tests appropriate to the data; (5) interpret statistical analysis; (6) produce high-quality reports.
Teaching and learning methods
Teaching in academic year 21/22 will be flexible and allow us to adapt to changing conditions, however, the common intention across units is to provide a blended offer of the best in online and on-campus teaching that includes: (1) media, activities and other learning material that should be engaged with before scheduled teaching; (2) a practical computing lab; (3) a weekly homework feedback session’ (4) opportunity for 1:1 support.
Knowledge and understanding
Understand some of the basic principles underlying statistical analysis, including: samples and populations, normal distribution, confidence intervals, statistical significance, hypothesis testing, and statistical measures of association; Understand the different levels at which social characteristics (variables) are measured and how resulting data are distributed;
Be able to interpret the findings of statistical analysis;
Read and interpret quantitative information in the form of tables and charts; Be able to produce basic descriptive statistics for a dataset; Be able, at an introductory level, to apply statistical tests appropriate to the data, including chi-square, t-tests, correlations; Have the skills necessary to produce reports using word-processing, including colour charts, tables and graphs in various software packages;
Transferable skills and personal qualities
Take an active approach to their learning, participate in class, and take responsibility for finding help for difficulties they encounter in the coursework.
- (i) analyse, critique and (re-)formulate a problem or issue; (ii) rapidly and thoroughly review/rate argument and evidence from targeted bibliographic searches; (iii) plan, structure and present arguments in a variety of written formats and to a strict word limit, (iv) express ideas verbally and organise work effectively in small teams for a variety of written and oral tasks; (v) obtain, manipulate and (re-)present different forms of data; (vi) manage time effectively; (vii) reflect on and improve performance through feedback.
The course is assessed by means of weekly homework submissions (20%) and a 2500-word project report (worth 80%) .
Formative feedback (both individual and collective) will be given on tasks and contribution in class. Summative feedback will be given on both assessed components via Blackboard (Grademark).
Kosuke Imai (2017). Quantitative Social Science: An Introduction. Princeton: Princeton University Press.
|Scheduled activity hours|
|Independent study hours|
|Reka Solymosi||Unit coordinator|
Across their course units each semester, full-time students are expected to devote a ‘working week’ of 35-40 hours to study. Accordingly each course unit demands 12-13 hours of study per week consisting of (i) timetabled teacher-led hours, (ii) preparation, required and further reading.
Open to BA (Criminology) students for which this subject is compulsory. LLB (Law with Criminology) if not choosing LAWS20412 or LAWS20692 can also take this module subject to availability of space (in the computer clusters we use). Also available to all students across Humanities subject to the availability of places, preference will be given to BASS students in the criminology pathway.
This course is available to study abroad students if they are able to demonstrate sufficient quantitative training ideally R software to engage successfully with the course.
We assume students have taken LAWS20441 Making Sense of Criminological Data or a course unit that covers similar material (such as SOST10021 Unequal Societies or SOS Applied Statistics). If in doubt, do not hesitate to contact the course director before enrolling. Students that have not taken a more basic data analysis course (such as those) beforehand will find the materials in this course unit very challenging. Although all the examples in this course are taken from the field of criminology, criminological knowledge is not a requirement for this course. In fact, this unit can be a good option for those UG (social science) students that want to benefit from an introduction to R.