Module manager: Dr Darren Greenwood
Email: d.c.greenwood@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2025/26
See programme entry requirements
| EPIB3035 | Research Methods for Clinical Sciences |
| MEDP5321M | Research Methods |
| YCHI5045M | Statistics for Health Sciences |
This module is not approved as an Elective
This module is designed to prepare you to conduct statistical methods commonly applied to health and social care research, including those that may be used in your research project later in the MSc degree programme. We take a pragmatic approach, avoiding mathematical derivations, allowing students quickly to apply methods to real-life health datasets.
Through lectures and practicals, you will gain a thorough understanding and practical experience of the statistical methods that are most often encountered in health and social care research settings.
This includes selecting the most appropriate method, assessing the validity of assumptions behind statistical models, using statistical software to conduct data analysis, and interpreting derived results appropriately.
Through lectures and practical work, you will also learn to interpret appropriate information derived from statistical analysis in published literature.
On successful completion of the module and associated assessment, you will have demonstrated the following learning outcomes relevant to the subject:
1. Identify the appropriate statistical test or modelling methodology to analyse data from a variety of situations and both observational and randomised controlled trial study designs;
2. Critically assess the validity of the assumptions behind the statistical methods;
3. Effectively perform this statistical technique in a computer package;
4. Select appropriate information to present their results clearly;
5. Critically interpret the results of statistical analyses;
6. Critically evaluate statistical methods reported in published work.
On successful completion of the module and associated assessment, you will have demonstrated the following skills learning outcomes:
1. Appropriate presentation of data in tables and figures:
Work ready skills: communication, technical/IT skills, problem solving and analytic skills, critical thinking and core literacies).
In addition, you will gain technical skills: selecting and generating appropriate tables and graphs.
2. Use of software for statistical analysis:
Work ready skills: technical/IT skills, problem solving and analytical skills, core literacies
In addition, you will gain Technical skills: Selecting, conducting and interpreting appropriate statistical methods.
3. Ability to critically assess published work:
Work ready skills: communication, problem solving and analytical skills, critical thinking, core literacies. Academic skills: reflection, critical thinking, academic language, ethics, information searching, referencing.
1. Hypothesis testing and confidence intervals, t-tests, and chi-squared tests
2. Non-parametric tests
3. Introduction to linear regression models
4. Logistic regression models
5. Model selection for causal inference
6. Introduction to prediction models
7. Survival analysis
8. Subgroup analysis, effect modification, and interaction terms
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 4 | 1 | 4 |
| Lectures | 4 | 1.5 | 6 |
| Practical | 8 | 1 | 8 |
| Seminar | 4 | 0.5 | 2 |
| Independent online learning hours | 5 | ||
| Private study hours | 125 | ||
| Total Contact hours | 20 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Independent online learning will mainly follow on from the formal classes and will make use of a portfolio of materials placed on the VLE. Students will also be expected to work in their own time, researching taught and online course work, building up their knowledge using the guidance provided by formal taught and online components of the module.
Staff-student interaction during each face-to-face session will enable informal monitoring of your progress in understanding how research design and statistical analysis relate, and particularly monitoring your data analysis skills during computer practicals (after each lecture). In addition you will be encouraged to keep a reflective log. This reflective log is kept during practical classes. The reflective log encourages good practice in keeping records of lessons learnt during practical sessions, and facilitates deeper learning by allowing you to reflect on your experience of conducting analysis, further developing your understanding after the practical sessions. It will also facilitate question generation during sessions.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Data Analysis workbook | 60 |
| Critique | Critical appraisal of published study | 40 |
| Total percentage (Assessment Coursework) | 100 | |
There are word counts for the summative components. Compensation is not permitted across summative components (e.g. a pass mark in all components of the assessment is required to pass the module). Resits will be in the same format as the original failed piece of coursework. Module marks will be capped at 50% on successful resit of any failed assessment in the module.
The reading list is available from the Library website
Last updated: 30/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team