Analysis of Repeated Measures
Repeated measures allow researchers to monitor how participants change over time. These repeated measures are correlated and require appropriate statistical modelling. In this course, you will understand when and why repeated measures data require appropriate analysis, and learn methods for analysing longitudinal/repeated continuous data.
| Dates | 5 - 6 June 2025 |
|---|---|
| Fee | £440 |
| Format | Online |
| Audience | Open to all applicants (prerequisites apply) |
Course profile
This course aims to provide an introduction to methods for analysing longitudinal/repeated continuous data, with a focus on methods for analysing individual trajectories over time. Throughout the course there will be an emphasis on what kinds of research question different types of method can be used to explore, and the interpretation of results.
Note, this course will not cover Generalized Estimating Equations (GEE).
Please click on the sections below for more information.
This 2-day course will take place online and consist of approximately 15 hours of teaching (including a mixture of live and pre-recorded lectures and live practical sessions).
Registration will start at 9:30am on the first day and the course will finish by 4.00pm on the final day.
By the end of the course participants should be able to:
- understand when and why repeated measures data require appropriate analysis;
- understand and be able to use various approaches to model linear and nonlinear trajectories of continuous outcomes, with specific focus on regression splines (for nonlinear trajectories);
- check the fit of models for continuous outcomes; and
- have a basic understanding of other approaches for nonlinear trajectory modelling: penalised regression splines, Superimposition by Translation And Rotation (SITAR), and latent trajectory models.
This course is intended for researchers who are, or will be, involved in analysing longitudinal data.
This course will cover:
- background: Why to collect, and how to analyse longitudinal data?;
- an overview of mixed-effects modelling approach;
- simple linear growth model for continuous outcomes;
- regression spline models for nonlinear trajectories in continuous outcomes; and
- penalised regression splines, SITAR, and latent trajectory models.
All teaching staff and tutors have extensive experience in performing analysis of repeated measures.
To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:
| Knowledge | You must be familiar with the algebra of standard regression models for continuous outcomes (to beyond the standard of the short course Introduction to Linear and Logistic Regression Models or to the level implied by module 3 of the Centre for Multilevel Modelling online multilevel modelling course). |
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| Software |
You must be familiar with R or STATA. Please note some practicals will only be available in R. Those using Stata* must have this installed in advance of the course. External participants are responsible for providing their own access to Stata, however if you are an employee of a university or another institution you may be able to get a short term free Evaluate license. If you are a student, Stata offer a short term free Student licence (one week). |
Before booking this course, please make sure you read the information provided above about the target audience and prerequisites. It is important that you have access to the relevant IT resources needed for the course and meet the knowledge prerequisites to ensure you can get the most from the course.
Bookings are taken via our online booking system, for which you must register an account. To check if you are eligible for free or discounted courses please see our fees and voucher packs page. All bookings are subject to our terms & conditions, which can be read in full here.
For help and support with booking a course refer to our booking information page, FAQs or feel free to contact us directly. For available payment options please see: How to pay your short course fees.
Participants are granted access to our virtual learning platform (Blackboard) 1 to 2 weeks in advance of the course. This allows time for any pre-course work to be completed and to familiarise with the platform.
To gain the most from the course, we recommend that you attend in full and participate in all interactive components. We endeavour to record all live lecture sessions and upload these to the online learning environment within 24 hours. This allows course participants to review these sessions at leisure and revisit them multiple times. Please note that we do not record breakout sessions.
All course participants retain access to the online learning materials and recordings for 3 months after the course.
University of Bristol staff and postgraduate students who do not wish to attend the full course may instead register for access to the 'Materials & Recordings' version of this course: Further information and bookings.
92% of attendees recommend this course*.
*Attendee feedback from 2025.
Here is a sample of feedback from the last run of the course:
"The lectures and practicals were all very clear and everything was explained well. I thought the pace was good, although I already have some experience of repeated measures analysis. There was a good mix of lectures and practicals. It is also useful that both R and Stata code is provided where possible." - Course feedback, June 2025
"The course was well-organised and ran smoothly, the materials and code provided were very helpful and explained the concepts in a good amount of detail." - Course feedback, June 2025
"The live lectures were amazing." - Course feedback, June 2025
"I felt the linear regression part was covered very carefully and with fantastic detail." - Course feedback, June 2025
"The mix of lectures and practicals is useful." - Course feedback, June 2025