Analysis of Repeated Measures

Coronavirus (COVID-19)

We may need to make responsive changes to our courses at short notice in order to follow the latest Public Health, Government and University guidance on coronavirus (COVID-19).


This course is temporarily suspended due to the Coronavirus pandemic. It is next expected to run in the 2021-22 short course programme.

Information on this page relates to the last run of the course and is for reference only. 

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Course dates

15 - 17 July 2020

Course duration

3 days, approximately 17 hours teaching.

Teaching will include introductory talks, lectures, worked examples and practical sessions using both Stata and Mplus software packages. Approximately 40% of the course will be practical work.

Registration will start at 9am on the first day, and the course will finish by 4pm on the final day.

Course tutors

Dr Jon Heron (course organiser) and others.

Course aims 

This course provides an introduction to methods for analysing longitudinal/repeated continuous data, with a focus on methods for analysing individual trajectories over time. Multilevel modelling and structural equation modelling (SEM) approaches will be described and compared. 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.

Course objectives

By the end of the course participants should be able to:

  1. understand when and why repeated measures data require appropriate analysis;
  2. understand and be able to use linear and simple non-linear models for modelling trajectories of continuous outcomes;
  3. check the fit of models for continuous outcomes; and
  4. understand and be able to apply latent class models for trajectories of continuous variables.

Who the course is intended for

The course is intended for researchers who are, or will be, involved in analysing longitudinal data. Participants should 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's online multilevel modelling course).

Please note: This is an advanced-level course, which will introduce MLM and SEM models, plus practicals using two different software packages (Stata and Mplus), in three days. Please be aware that whilst we assume no previous experience of Mplus, this course may prove challenging for those who are not fairly fluent in Stata.

Course outline

  • Background: Why collect, and how to analyse longitudinal data?
  • Overview of multilevel and structural equation approaches
  • Simple linear growth model for continuous outcomes
  • More complex models for continuous outcomes
  • Mixture modelling including Growth Mixture Modelling (GMM)
  • Methods for incorporating covariates/outcomes in both LGMs and mixture models.

Please note: Practical sessions of this course will be held in a computer lab, so you will not need to bring a laptop. The course will use Stata, Mplus Demo (version 8 or above) and MLwiN (any version).

Recommended reading

  • Goldstein H and De Stavola B. Statistical modelling of repeated measurement data. Longitudinal and Life Course Studies 2010; 1(2): 170-185.
  • Curran PJ, Hussong AM. The use of latent trajectory models in psychopathology research. Journal of Abnormal Psychology 2003; 112: 526–544.

The course was really well-organised, and I felt that my understanding of multilevel modelling and structural equation modelling approaches increased greatly.

Course feedback, February 2019

Course fee


More information on course fees, fee waivers and reduced prices.

Course refreshments

We provide morning and afternoon refreshment breaks, including tea and coffee, biscuits and fresh fruit.

If you have specific dietary needs we ask that you let us know in advance.

Lunch is not included. There are a range of local cafes and supermarkets nearby for students to purchase lunch. 

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