Introduction to Multilevel Modelling Using MLwiN, 26-28 January 2021, Online Webinar via Zoom

Organised by

Centre for Multilevel Modelling, University of Bristol


Professor George Leckie and Professor William Browne


26/01/2021 - 28/01/2021


Event will be held online via Zoom.


Lucy Haslam,


Introduction to Multilevel Modelling Using MLwiN, 26-28 January 2021, Online Webinar via Zoom 



Professor George Leckie and Professor William Browne 


This three-day course provides an introduction to multilevel modelling and includes software practicals using the MLwiN software. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or hierarchical). These models can be viewed as an extension of conventional linear and logistic regression models to account for and learn from the clustering in the data. Such models are appropriate when, for example, analysing exam scores of students nested within schools, or health outcomes of patients nested within hospitals. Special interest lies in disentangling social processes operating at different levels of analysis by decomposing the within- from the between-cluster effects of covariates (explanatory variables). Longitudinal data are also clustered, with repeated measurements on individuals or multiple panel waves per survey respondent. Throughout the course we emphasize how to interpret the models and to decide on what kinds of research question they can be used to explore. 


  1. Overview of multilevel modelling 
  2. Introduction to MLwiN 
  3. Variance-components models 
  4. Random-intercept models with covariates 
  5. Between- and within-effects of level-1 covariates 
  6. Random-coefficient models 
  7. Growth-curve models 
  8. Three-level models 
  9. Review of single-level logistic regression 
  10. Two-level logistic regression 

The course will consist of a 2:1 mix of lectures and hands-on practical sessions applying the taught methods to real datasets. The instructors alternate the lecturing. Each lecture is immediately followed by a practical giving participants the chance to replicate the presented analyses and to consolidate their knowledge. The lectures are software independent. The practicals are self-directed and use MLwiN: participants complete them at their own pace. In both the lectures and practicals participants have opportunities to interact with the instructors. 

The course will be delivered online via the freely accessible Zoom platform. The lectures will be delivered live. Participants can ask questions via Zoom’s text-based Q&A facility that will be monitored and answered by one of the instructors not presenting or relayed to the instructor presenting to answer live.  

Participants are encouraged to join the lectures live, but recordings of the lectures will be made available shortly afterwards for two weeks following the course if participants are unable to attend at the scheduled time. After two weeks, video access will end and can’t be extended. 

During the practicals, participants can speak 1:1 with the instructors in short Zoom meetings. Participants can use these opportunities to ask specific questions about the course material or about multilevel modelling related to their own research. 

Participants will be emailed in advance with comprehensive PDF copies of the lecture slides and practical handouts. They will also be provided with the teaching version of MLwiN and the MLwiN worksheets (datasets) to replicate the course materials. Participants are encouraged to view the lecture slides and practical handouts on a second screen (or tablet etc.) during the live lectures and self-directed practicals, else print copies out to have in front of them. 

MLwiN is dedicated multilevel modelling software developed by our research team over the last 25 years. On this course we will be using the free teaching version of MLwiN. This version works with all the datasets used on the course and a wide range of other teaching datasets which come with the software. We will email you the teaching version prior to the start of the course. 

Should you wish to use MLwiN after the course with your own data, you will need to use the regular version of MLwiN. This is free to UK academics (but without user support) reflecting long periods of funding from the UK’s Economic and Social science Research Council (ESRC). For all other users there is a 30-day trial version, but after that you will have to purchase MLwiN if you wish to continue using it to analyse your own data. There are various price options available.

MLwiN is Windows software, but can be run on Mac via the Wine software or through a virtual machine. Note that Wine will only work on versions of MacOS prior to Catalina.

We assume no prior knowledge of multilevel modelling or MLwiN. However, participants should be familiar with estimating and interpreting linear regression models, including the writing and interpretation of model equations, hypothesis testing and model selection, and the use and interpretation of dummy variables and interaction terms. 

In order for participants to start to become familiar with MLwiN, we will email in advance a video lecture which provides an introduction to the software in terms of fitting linear regression models. We will also provide a practical with step-by-step instructions to allow you to replicate the presented analyses. Youshould run through this to confirm that MLwiN is working correctly on your computer and to further familiarise yourself with using MLwiN. 

Some participants may wish to further refresh themselves of linear regression by reading module 3 of our LEMMA online course.

The course starts and ends each day at 09:30 and 16:00 with a 15-minute morning break and a one-hour break for lunch from 13:00 to 14:00. 


  • For UK-registered MSc and PhD students - £180 
  • For UK university academics, UK public sector staff, and staff at UK registered charity organisations - £360 
  • For all other participants - £660 

Please note, in order to be eligible for the reduced pricing brackets please submit your application using your UK academic/organisational email address. 

A full refund will be given if cancellation occurs three weeks prior to the event. No refund is given after this date. By completing the application form, you are accepting these cancellation terms. 

Our workshops are now regularly over-subscribed so we have had to introduce an application and selection process. If you would like to attend the workshop, please complete and submit the online application form (see below). Please note the closing date for applications is 29th November 2020. 

Submission of the form and its acknowledgement does not guarantee a place on the workshop. We will email you by 7th December to tell you whether or not your application has been successful. If you are offered a place on the workshop, it will not be confirmed until you have accepted and paid the relevant fee. 

If you have any queries, please email

Go to booking form >> 

Terms and conditions 
Please click here to read the booking terms and conditions before completing the booking form. Note that it is the participant’s responsibility to ensure that the Zoom and MLwiN software work on their computer in advance of the course, as the Centre for Multilevel Modelling is unable to provide technical support. 


Entry (no or almost no prior knowledge)


For UK-registered MSc and PhD students - £180
For UK university academics, UK public sector staff, and staff at UK registered charity organisations - £360
For all other participants - £660

Please note, in order to be eligible for the reduced pricing brackets please submit your application using your UK academic/organisational email address.

Website and registration




Quantitative Data Handling and Data Analysis, Multilevel Modelling , Hierarchical models, Mixed models, Random effects, Longitudinal Data Analysis, Growth curve models

Related publications and presentations

Quantitative Data Handling and Data Analysis
Multilevel Modelling
Hierarchical models
Mixed models
Random effects
Longitudinal Data Analysis
Growth curve models

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