2026/27 Taught Postgraduate Module Catalogue

MEDP5344M Quantitative Research Methods

15 Credits Class Size: 30

Module manager: Prof Richard Feltbower
Email: r.g.feltbower@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2026/27

Pre-requisite qualifications

See programme entry requirements.

Module replaces

MEDP5321M Research Methods

This module is not approved as an Elective

Module summary

The module will enable you to understand, apply and critique existing research methods, focusing on quantitative analyses. It will demonstrate why many traditional “statistical tests” are no longer fit for purpose and introduce the concept of causal inference methods to carry out robust analyses of observational data and randomised clinical trials.

Objectives

This module is designed to prepare you to carry out your research project later in the degree programme.

The module is also designed to allow you to make judgements about the quality of the peer reviewed literature base, to understand the concepts of research and data analysis and how to present and interpret appropriate information derived from statistical analysis.

Learning outcomes

On successful completion of the module students will be able to:

1. Recognise and apply the main statistical tools used in clinical research, for i) evaluation of the work of others and ii) analysis of your own results

2. Design appropriate experiments/studies and apply appropriate statistical methods for evaluation

3. Use Stata software to process data appropriately and carry out statistical analysis proficiently.

4. Apply causal inference methods and identify confounding factors for observational data.

Skills outcomes

On successful completion of the module students will be able to:

1) evaluate and apply appropriate statistical methods using Stata to analyse quantitative datasets and interpret results in a research context.

2) critically assess and review published work.

Syllabus

Quantitative research study designs;

Data collection and coding;

Summarising and presenting data;

Sampling distributions and confidence intervals;

Correlation, simple linear regression and effect estimation;

Non-parametric methods;

Causal inference methods and confounding;

Multivariable linear regression modelling;

Critical appraisal of the medical literature.

Teaching Methods

Delivery type Number Length hours Student hours
Lecture 8 1 8
Practical 6 1.5 9
Seminar 3 2 6
Independent online learning hours 55
Private study hours 72
Total Contact hours 23
Total hours (100hr per 10 credits) 150

Opportunities for Formative Feedback

Student progress will be monitored through classroom discussion with tutors and through feedback from Stata practical questions. This feedback will be both oral, in the work sessions, and written for practice problems. Some feedback will be through the VLE/Minerva and there will be a dedicated revision tutorial near the end of the module for students to clarify any questions they may have and to gain experience of the summative assessment based on a past exam paper. Students will also participate in group work to carry out a critical appraisal of a journal article, culminating in a short 10min presentation; feedback will be provided orally after the presentation.

Methods of Assessment

Coursework
Assessment type Notes % of formal assessment
Critique Oral Presentation of journal article appraisal, carried out in group work; feedback will be provided orally after the presentation. 0
Problem Sheet Questions based on analysis of a Stata dataset comparing correlation and simple linear regression analyses; written feedback will be provided by email. 0
Total percentage (Assessment Coursework) 0

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams
Exam type Exam duration % of formal assessment
Unseen Practical exam (Semester 2) 2.0 Hrs Mins 100
Total percentage (Assessment Exams) 100

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading List

Check the module area in Minerva for your reading list

Last updated: 09/06/2026

Errors, omissions, failed links etc should be notified to the Catalogue Team