Admission requirements
Only students of the MSc Crisis and Security Management can take this course.
Description
Researchers and policy makers in security studies are ultimately interested in causal questions: Does increased police presence reduce crime and improve perceptions of safety? Do major revelations about intelligence activities disrupt public trust in security institutions? How do terrorist attacks affect attitudes toward minorities or levels of hate crime? And do peace interventions alter conflict trajectories, or merely pause violence?
Strictly speaking, causal inference is only possible in experimental settings with random assignment. In security research, however, such experiments are often infeasible or unethical—we cannot randomly expose subjects to police violence, terrorism, or war. This course therefore introduces quasi-experimental designs, or natural experiments, as practical alternatives to the experimental “gold standard.” Students will learn the basics of implementing these methods using the R programming language, with an emphasis on hands-on learning.
The course combines conceptual foundations with applied exercises and equips students with tools for conducting basic but robust causal analyses. No prior programming experience is required, though basic statistical knowledge is helpful. The course is ideal for everyone who wants to improve their knowledge of quantitative methods. Students with limited quantitative background are encouraged to review key concepts before the course begins; suggested resources are provided in the syllabus.
Course Objectives
After finalising this course, students will be able to:
Identify and differentiate the most important quasi-experimental research designs to study the causal effects of security policies.
Critically evaluate the strengths and limitations of these designs.
Apply the core logic of these designs to analyse security-related policy questions.
Timetable
On the right side of programme front page of the studyguide you will find links to the website and timetables, uSis and Brightspace.
Mode of Instruction
This course consists of 7 lectures. Classes will be dedicated to lecturing and group work. Students are required to bring their own device to class to complete hands-on exercises using the R programming language.
Total study load 140 hours:
21 Contact hours.
119 Self-study hours: self-learning, reading, assignments, etc
Assessment method
Assessment for this course is based on two assignments:
Group Assignment
30 % of final grade
Grade can be compensated in case of a fail grade (< 5.50)
Resit not possible
Individual exam
70% of final grade
Grade cannot be compensated, a 5.50 is required to pass the course.
Resit is possible
Resit will take the same form.
Additional, formative (non-graded) assignments are an obligatory part of the course. The calculated overall course grade must be at least 5.50 in order to pass the course. If the calculated overall course grade is lower than 5.50, students are also permitted to resit the 70% individual exam. In the case of written assessment methods, the examiner can always initiate a follow-up conversation with the student to establish whether the learning objectives have been met.
Reading list
A selection of textbooks and other readings, to be announced on Brightspace.
Registration
Please note, registration for block 4 electives will be organised by the OSC in a different way from the regular course registration for semester 2. More information about this will follow in the beginning of semester 2.
Please note: Registration for the resit of an exam (and exam) is mandatory, this has to be done by the student and can be done from Monday 4 May 2026 until 10 days before the exam. Until 5 days before the exam you can email OSC and fill in a form.
Contact
dr. Christof Nägel c.r.e.nagel@fgga.leidenuniv.nl