POLS0013: Measurement in Data Science
UCL Department of Political Science
School of Public Policy
Term 1 - Academic Year 26-27
Introduction
This website is currently undergoing reviews in preparation for 26-27.
Welcome to the course website dedicated to the POLS0013 module Measurement in Data Science! On this site, you will find the lecture slides as well as the seminar tasks. You can navigate to each week’s material by clicking on the specific sidebar menu link. Please do let me know if you are struggling to access anything. Below you will find information related to the organisational aspects of the course. These are also covered in the intro lecture video.
Course Description
This module is designed for third year students in the undergraduate degrees in Philosophy, Politics and Economics, in Geography, in Population Health and in Social Sciences with the focus on Social Data Science. It therefore assumes that you are familiar with the material in the required first and second year modules of those programmes. These preceding modules cover basic quantitative analysis, sampling, linear regression, regression models for binary and categorical outcomes (especially logistic regression), panel data and multilevel models. The module is now also open to third year students in the undergraduate degree in Politics and International Relations.
This module is fundamentally about the task of connecting the data that we use in quantitative analyses with the social science concepts that we are interested in making claims about. The processes of conceptualisation and measurement are sometimes used to distinguish between two parts of what we will be studying. In his book “Conceptualization and Measurement in the Social Sciences”, Hubert Blalock writes “Conceptualization involves a series of processes by which theoretical constructs, ideas, and concepts are clarified, distinguished, and given definitions that make it possible to reach a reasonable degree of consensus and understanding of the theoretical ideas we are trying to express.” (Blalock 1982, 11) “By measurement, we refer to the general process through which numbers are assigned to objects in such a fashion that it is also understood just what kinds of mathematical operations can legitimately be used, given the nature of the physical operations that have been used to justify or rationalize this assignment of numbers to objects.”
Measurement is important whether we are making causal claims or descriptive claims, at least if we want the evidence we collect to speak to underlying concepts of theoretical interest. It is common, but unfortunate, to have “slippage” between the analysis that researchers have done and how they talk about it. It is always tempting to report that you have shown something about a grand social scientific concept, conveniently forgetting that all you have actually done is demonstrated something about your dubious measure of that concept. One of the things that you learn in a module on causal inference is about a similar sort of slippage, whereby analyses that do not justify causal claims are discussed as if they do.
Note that the material of this module, similarly to causal inference modules, is that it does not always involve new estimation techniques. It often just involves careful thinking about which analyses to do, some of which may prove to be very simple. Some of the techniques covered in this module involve applying familiar regression analyses to data in a particular way so as to solve a measurement problem, much like several “causal inference methods” involve applying familiar regression analyses to data in a particular way (eg regression discontinuity designs and differences-in-differences). While some of the topics will involve new estimators and models, not all of them will. At several points in the module, we will define a problem, observe that if we had some types of data we could solve the measurement problem with a regression model, but that if we had different types of data, we would need a new method. As with causal inference, a lot of the core intellectual content here is figuring out what is possible and sensible with the data you have, not necessarily learning some fancier model that will magically make the limitations of the data go away.
Teaching Delivery
We recommend you structure your weekly workflow as follows:
- Complete the required readings
- Start the seminar assignment
- Attend the lecture
- Continue on the seminar assignment
- Attend the seminar
- Go back through the seminar assignment and complete it. Look at and work through the solutions once released.
- Throughout the above, make note of things that are still unclear. If they are not clarified by any of the above, send a question via the Moodle Forum, ask in the lectures or seminars or come to SSF hours.
Lectures
Lectures will take place Tuesdays 9-11am. The lecture slides will be made available to you to download before the lecture on this website in the tab dedicated to the relevant week.
Seminars
The one-hour seminars will take place in person on Tuesday afternoons. They will begin with a 10-15 min interactive quiz recapping the main take-aways of the lecture, participation in which counts towards your participation grade.
The remainder of the time will be dedicated to discussing the weekly problem set. 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. Therefore, each week, you will complete a problem set which involves applying material from the course, writing code in the R programming language and interpreting the results. The problem sets should be attempted before the seminar and you should come prepared with your code and written draft answers to the seminars.
While these formative assessments do not count toward the final mark, they provide an opportunity for peer and instructor feedback, as well as ample time to ask questions about the problem set and particular issues that relate to coding in R. They are designed to prepare you both for implementing these methods, should you wish to, in your dissertations or any other future work, as well as for the exam which will require you to understand what the code is doing and provide interpretations for its outputs. This is precisely the skills that completing these seminars helps you develop. All assignments will be available on the course website in the tab corresponding to the week’s lecture, and annotated solutions will be released (also via the course website) around midday on Thursdays after the seminar.
Attendance during these seminar hours is mandatory and we will take a register at the beginning of the session. Please try to stick with your assigned seminar slot, such as to keep an even numbers distribution across the groups. If this is not possible, you can ask the Political Science undergraduate admin team ( polsci.ese@ucl.ac.uk) for help. Note that the course convenor cannot help you with timetabling issues.
Assessment
10% of your grade comes from in-class participation and is just pass-fail. To pass, you must have participated in seven out of ten in-class quizzes.
Students will write a 1500 word “essay” and take an exam, each worth 45% of the grade. The essay involves finding and critiquing a pre-existing measure of a social science concept. The exam involves interpreting the outputs (and inputs) of data analyses on a given dataset, in the style of the weekly problem sets. You will write a mock exam in lieu of the usual seminar activity in week 9.
- Assessment part I (“essay”) due end of term 1
- Assessment part II (“exam”) held beginning of term 2
Please remember that plagiarism is taken extremely seriously and can disqualify you from the module (for details of what constitutes plagiarism see . If you are in doubt about any of this, ask the tutor.
You can download the instructions for the first part of the assessment (research report on a measure) below.
Resources
Readings
This module combines a range of material that is not traditionally taught together (although it should be). As a result, Prof. Ben Lauderdale is in the process of writing a textbook for the module, Pragmatic Social Measurement, which provides most of the required readings for the module. The latest draft version of this book is available here:
You will find it useful to consult additional reference materials, and there are no shortage of possible references for most of the individual topics covered in the module. For each week in the schedule below, I list further readings on the theoretical ideas covered that week as well as applications of the resulting measurement techniques.
As a supplemental resource for several of the topics in the module, we highly recommend “An Introduction to Statistical Learning” (2nd Edition) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. The PDF of the textbook is freely available for download. We will not cover all the topics in that book, because it aims to provide an introduction to “statistical learning” aka machine learning, and our aim in this module is to study measurement. Nonetheless, the topics in that book that are not covered in this module are well worth your time.
Software
Throughout the course we will use the free and open source statistical analysis software R.
Before the course starts, you can and should download and install (the latest version of) R on your personal computer. You should also also download and install RStudio, which is a user-interface to R. If the last time you downloaded R and RStudio dates back more than 1 year, we highly recommend you re-download them.
Please ensure that both R and RStudio are installed on your personal computers before the first lecture. This is recommended over using the UCL RStudio Server, which is only accessible online. UCL machines, either virtual via Desktop@UCL or on campus, will already have this software installed.
Academic Freedom and Intellectual Property
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: 12 Oct 2026 1:24 PM BST