BASS Social Anthropology and Criminology

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.

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.

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.

 

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 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.

• Homework portfolio: weekly quizzes - (worth 20%);

• 2500 word project (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 the Virtual Learning Environment.

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

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.