Course unit details:
Modelling Criminological Data

Course unit fact file
Unit code CRIM20452
Credit rating 20
Unit level Level 2
Teaching period(s) Semester 2
Offered by Criminology
Available as a free choice unit? Yes

Overview

Data is ubiquitous today and affects all aspects of everyday life. This course aims to provide the student with 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 and claims made by politicians. 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.

Pre/co-requisites

Unit title Unit code Requirement type Description
Making Sense of Criminological Data CRIM20441 Pre-Requisite Compulsory
Making Sense of Criminological Data CRIM14442 Pre-Requisite Compulsory
CRIM20452 course requirement

Compulsory for BA (Criminology) students.  LLB (Law with Criminology) if not choosing CRIM20692 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 Criminology pathway students. 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.

Pre-requisites:  The course assumes the student has already taken and introductory statistical/ data course such as CRIM20441/CRIM14442 Making Sense of Criminological Data or the equivalent in other departments across the School of Social Sciences. In case of doubt about whether you meet this criterion do not hesitate to contact the Course Unit 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 Sciences) students that want to benefit from an introduction to R.

Aims

The unit aims to:

(i) Enhance students' ability to explain and relate crime data to criminological theory; 
(ii) Develop students' practical skills in data handling and visualisation; 
(iii) Develop students’ ability to communicate statistical findings clearly and accurately; 
(iv) Examine knowledge and understanding of core statistical concepts.

 

Learning outcomes

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 and learning across course units consists of: (1) preparatory work to be completed prior to teaching sessions, including readings, pre-recorded subject material and online activities; (2) a weekly whole-class lecture or workshop; (3) a tutorial; and (4) one-to-one support via subject specific office hours.

Employability skills

Other
(i) analyse, critique and (re-)formulate a problem or issue; (ii) Create reports that present statistical findings using appropriate terminology, visuals, and formatting; (iii) plan, structure and present arguments in a variety of written formats and to a strict word limit; (iv) Explain key statistical concepts and principles used in quantitative (criminology) research;

Assessment methods

  • Homework portfolio: weekly quizzes - (worth 20%);
  • 2500 word project (worth 80%);

     

Feedback methods

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 the Virtual Learning Environment.

Recommended reading

Kosuke Imai (2017). Quantitative Social Science: An Introduction. Princeton: Princeton University Press.

Study hours

Scheduled activity hours
Lectures 10
Practical classes & workshops 10
Tutorials 10
Independent study hours
Independent study 170

Teaching staff

Staff member Role
Ana Maria Nicoriciu Unit coordinator
Thiago Oliveira Unit coordinator

Additional notes

Across their course units each semester, full-time students are expected to devote a 'working week' of around 30-35 hours to study. Accordingly each course unit demands around 10-11 hours of study per week consisting of (i) 3 timetabled teacher-led hours, (ii) 7-8 independent study hours devoted to preparation, required and further reading, and note taking.

 

Fees and funding

Fees

Tuition fees for home students commencing their studies in September 2027 will be £10,050 per annum.

For general information please see the undergraduate finance pages.

Policy on additional costs

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

Scholarships/sponsorships

We know that student finance can be complicated. The links below provide further information to help guide you.

Learn more about student finance options for UK students.

Learn more about fees and finance for international students .

As an international student you may be eligible for our Global Futures Scholarships . This is open to students starting their studies in September 2026.

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