Module code: POL2045

Module Overview

Econometrics is a research tool that involves the application of statistical methods for the analysis and critical evaluation of datasets. Econometrics is used by social scientists, particularly politics, economics, sociology, in order to explore competing hypotheses and generate predictions applied to real world puzzles. This module is designed for Level 5 undergraduate students specializing in politics and economics who want to work with quantitative data, gain a substantive understanding of the theory and practice of econometrics, learn how to use statistical inference and econometric analysis, and build awareness and expertise in testing hypotheses relying on evidence-based research. Understanding econometrics is recognized by economists and quantitative political scientists as an essential skill, which allows students to obtain an understanding and appreciation of econometric research applications, develop the ability to critically evaluate existing econometric research and policy analysis, and gain the skills to identify shortcomings and problems with studies and assess the confidence that should be placed in the proposed recommendations and conclusions.

This module will also provide students with the skills necessary and the practical experience of using quantitative data and techniques of analysing it. This way students will learn new in-demand skills, not only for their studies but also skills they can highlight in their CVs.

The course uses the statistical software RStudio, and you will learn how to critical evaluate research questions by using them most important surveys available. Students will also learn skills they can apply into assignments during the final year of their studies an in the job market.

Module provider


Module Leader

NEZI Roula (Politics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

Module cap (Maximum number of students): N/A

Overall student workload

Workshop Hours: 4

Independent Learning Hours: 99

Lecture Hours: 11

Seminar Hours: 11

Tutorial Hours: 3

Guided Learning: 11

Captured Content: 11

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

The course consists of lectures and small-group seminars organized as labs. The lectures provide an introduction to causal inference, quantitative research design, descriptive statistics, probabilities and the normal distribution, hypothesis testing, bivariate regression, multiple regression, problems in multiple regression, binary choice models and a brief overview of more advanced topics. The students will be provided with a dataset that will be used along the course in the seminars or lab sessions where the statistical software R is available.

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework RESEARCH DESIGN (1000 WORDS) 10

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate a comprehensive understanding of the main principles of a research design in Social Sciences and be able to successfully apply and describe the concepts covered during the lectures to the research topic of their choice. In addition, students will also have to demonstrate their ability to conduct independent quantitative analysis using R, identify the appropriate statistical method to be used to answer specific research questions and interpret the results obtained.

Thus, the summative assessment for this module consists of:

Research Design 

Statistical Assignment I 

Statistical Assignment II 

Formative assessment and feedback

Students will receive written feedback for all the assignments submitted and they can also make use of the office hours discuss further problems related to the module and their content.

Module aims

  • The ability to develop a basic understanding of econometrics at the theoretical level
  • The hands-on experience of completing practice exercises using R
  • The capability to individually engage in applied analysis of cross sectional data.
  • The appreciation of the strengths and problems of different statistical methods

Learning outcomes

Attributes Developed
001 Read, understand, and evaluate quantitative analysis published in top journals PT
002 Identify the appropriate statistical method to be used to answer specific research questions KCPT
003 Use a variety of statistical methods and tools KCP
004 Perform and interpret quantitative data analysis using R KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Provide students with the theoretical tools to read, understand, and critically evaluate quantitative analysis published in academic journals and; provide students with the practical skills required to conduct independent quantitative research.

The learning and teaching methods include: 

Lectures, Seminars and statistical application labs

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: POL2045

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Politics and Economics BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.