Course Outline

This course is designed to introduce you to and help you become familiar with tools of quantitative data analysis for the social sciences. The introductory course has two primary aims. First, students will be introduced to statistical models that researchers and policymakers use in answering social, political, and economic questions. Second, the course will equip students to use one or more of the discussed techniques in their MSc dissertation. By the end of the course, students should be able to understand the quantitative tools employed in political, social, and economic research; to perform data analysis using the statistical software R and interpret results; and to fruitfully employ introductory quantitative methods in their dissertation research and in subsequent careers.

This module (or the Advanced Quantitative Methods module) is required of all students pursuing an MSc from the School of Public Policy, including degrees in Democracy and Democratization, European Public Policy, Global Governance and Ethics, International Public Policy, Public Policy, and Security Studies.


Teaching delivery is split into two different components.


All of the main course content will be delivered during the weekly two-hour lecture, which will be recorded. The lecture recordings will be available on the module Moodle page. The lecturers for this module are Dr Michal Ovádek and Dr Indraneel Sircar.


This is a practical module, and a key learning objective is for students to be able to implement the statistical methods we cover during lectures to real data. Each week, you will complete a problem set which involves writing code in the R programming language (see below for more details) and interpreting the results.

The goal of these seminars is to provide you with ample time to ask questions about the problem set, and particular issues that relate to coding in R. During your allocated seminar time, you will be able to ask questions of the teacher either by message or by requesting a video call; speak with other students about the problem set; and watch short live demonstrations from your seminar teacher. Attendance during these seminar hours is mandatory and we will take a register at the beginning of the session.

Course Assessment

The course has two marked components, an online midterm assessment (worth 30% of the course mark) and a final take-home assessment (worth 70% of the course mark).

The midterm coursework will review basic theory – testing whether students have done all the required reading and the assignments – and also include a practical component which will require students to complete tasks using R. The midterm will take place during Reading Week.

The final take-home assessment will also cover both theory and practical questions, and will require students to address specific research or policy questions using real-world datasets. Working from data that we provide, you will be asked conduct various statistical analyses using R, and also to produce substantive responses to the questions posed. The final assessment will be due on 10 January 2024 at 2pm.

Essay deadlines

Department policy requires that penalty points are deducted for essays that are late and does not allow individual lecturers to grant extensions. If you experience any difficulties that mean you are not able to study to the best of your ability and struggle to meet deadlines, then you should speak to your personal tutor for help filling out and submitting an Extenuating Circumstances Form.

Essay word limits

Department policy requires that penalty points are deducted for essays that exceed the maximum word limit.


You are reminded that it is an academic offence to use the work of others without acknowledgement. You should note that UCL uses a plagiarism detection system (TurnItIn) to scan work for evidence of plagiarism. This system gives access to many sources worldwide, including websites and journals, as well as work previously submitted to the department, UCL and other universities. Students may submit their own work to TurnItIn prior to handing it in to see for themselves whether they have inadvertently breached the rules regarding the appropriate use and citation of sources. You can find more information about plagiarism and TurnItIn at this link.

Class Assignments

Before each class, students are expected to review the material on the course website and attempt to understand and implement the code provided for the in-class exercises. Failing to try to familiarise yourself with the relevant code will mean that the classes progress much more slowly, and you will have fewer opportunities to ask substantive questions of the teaching fellows.

After each class, students are expected to complete the at-home exercises. While these assignments do not count toward the course mark, they will serve as a very good guide to the assessed coursework later in the course. We strongly encourage all students to complete these assignments in advance of the solutions being released each Monday.

Course Resources


The main textbooks we will use on the course are:

These books provide a useful mix of quantitative and statistical background and theory with plenty of real-world applied examples. There is also a lot of R code integrated throughout the book in Imai (2017). The books are available online through UCL library.

We may occasionally assign additional readings for certain topics, and these will be made available online through the course’s electronic reading list and through the course webpage. It is expected that students will have read all the required reading prior to coming to lecture and seminar.


We will make extensive use of UCL’s virtual learning platform, Moodle. Students will be automatically enrolled in Moodle for the course to which they have been assigned.

We will use Moodle as the primary platform for managing communications for this course, particularly through the Discussion Forum that is listed on the Moodle page for this course. This is a much more efficient mode of communication than e-mail because it allows you to answer each other’s questions, which will be much faster than waiting for a response from us, and for the entire class to see our responses, ensuring that we do not answer the same question multiple times over e-mail. Note that we expect you to use Teams for both student-to-student and student-to-tutor communication, meaning that you should be attempting to answer each other’s questions.

Note that we will not be answering substantive questions over e-mail. If you ask us a substantive question via e-mail, we will simply ask you to post it on Moodle. All questions that are of administrative or technical nature should be addressed to us before or after lecture or during our office hours.

Course website

All materials for the course will be hosted here on the dedicated course website ( This site will include lecture slides, class assignments, and homework tasks. Each week we will add new material to the site, and we expect you to review this material before each class.

R and RStudio

Every quantitative social scientist needs to know how to operate at least one piece of statistical software. In this course, we will be teaching you how to use R. R is statistical software that allows one to manipulate data and estimate a wide variety of statistics. It is one of the fastest growing statistical software packages, one of the most popular data science software packages, and, importantly, it is open source (free!). We will also be using the RStudio user-interface, which makes operating R somewhat easier. You should download and install both R and RStudio on your personal computers before the course starts. The latest version of RStudio can be downloaded here and R can be downloaded here.

It is important to note that we do not expect any student to have prior programming experience. We will teach you R during the course. That said, we do expect students to try and complete the homework assignments each week, and to at least attempt to work through the class materials before class. This will make the seminars more engaging, as you will spend less time working on trivial technical details, and more time talking about the substantive importance of the statistical results.

Academic Freedom

Academic freedom is the cornerstone of university research and teaching, so that all university staff, speakers, and students can freely explore questions and ideas and challenge perceived views and opinions, without being censored or harassed by a government, any state authorities, the University, other students, or external pressure groups. As part of the UCL academic community, all staff, speakers, and students share these responsibilities:

  • Everyone must respect freedom of thought and freedom of expression. Your lecturer will not limit what can be discussed in the seminar, as long as it is relevant to the subject. They will not censor any topics, and they will expose you to controversial issues, questions, facts, views, and debates.

    • You may disagree with some facts or views that you read or hear in the classroom. You are encouraged to engage with these facts and views in a respectful manner.

    • Your lecturer will not penalise you merely for expressing views they or other students disagree with. However, they will expect you to present logical arguments supported by evidence.

  • You are explicitly prohibited from recording, publishing, distributing or transferring any class material/content, in whole or in part, in any format, to any individual or entity outside the module, linking to or posting it online (including social media), or making it otherwise available to any person or entity outside the module, unless you have received prior specific written approval from the module leader. You are also explicitly prohibited from aiding or abetting in any of these actions. Similarly, your lecturer will not record, publish or distribute seminar sessions without the explicit consent of the participants.

  • By agreeing to take this module, you agree to abide by these terms. If you do not comply with these terms, you will potentially be subject to disciplinary actions similar to those under violations of the university Student Code of Conduct.

Last Updated: 11 Dec 2023 9:50 AM GMT