Training and Events
Multilevel Modelling courses 7th-11th January 2019
|University of Bristol|
Professor Harvey Goldstein
07/01/2019 - 11/01/2019
Introduction to Multilevel Modelling Using MLwiN,
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Alyce Griffith, email@example.com. 0117 33 14291
The Centre for Multilevel Modelling will be running two workshops at the beginning of January. These can be attended as stand-alone courses, however a 25% discount will be given to participants attending both.
Introduction to Multilevel Modelling Using MLwiN, 7-9 January 2019, University of Bristol
This three-day course provides an introduction to multilevel modelling using the MLwiN software. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered or hierarchical. Such methods are appropriate when, for example, analysing the exam scores of students nested within schools, or the health outcomes of patients nested within hospitals. Longitudinal data are also clustered, with repeated measurements on individuals or multiple panel waves per survey respondent.
The course will consist of a 2:1 mix of lectures and hands-on practical sessions applying the taught methods to real datasets using MLwiN. Throughout, there is an emphasis on how to interpret the models and on what kinds of research question they can be used to explore.
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. Some participants may wish to refresh themselves of this material by reading module 3 of our LEMMA online course. https://www.bristol.ac.uk/cmm/learning/online-course/course-topics.html.
Professor Harvey Goldstein, Dr George Leckie
The course starts with registration at 10.45 on Day 1 and finishes at 15:00 on Day 3. Participants are expected to attend the full course.
Handling missing data for multilevel models, 10-11 January 2019, University of Bristol
This two-day course can be attended as a stand-alone course for those already familiar with multilevel modelling or as a continuation for the introductory course on multilevel modelling that immediately precedes it.
The workshop will provide an introduction to this important topic together with a hands-on experience for participants in fitting data with complex patterns of missingness. It will utilise the recently released missing data features in the Stat-JR software distributed along with MLwiN by the Centre for Multilevel modelling, Bristol. This software is designed to handle very general data structures, including multilevel ones, and is applicable to both continuously distributed data and categorical data. It will cover traditional multiple imputation techniques using joint modelling and fully conditional modelling but with an emphasis on more recent and more flexible Bayesian models.
The workshop will consist of a mixture of explanatory talks and demonstrations together with ‘hands-on’ data analysis. If users wish to bring their own datasets (preferably not too large) there will be time to analyse these with help.
General information on Stat-JR: https://www.bristol.ac.uk/cmm/software/statjr/
Stat-JR manuals: https://www.bristol.ac.uk/cmm/software/statjr/manuals/
Missing data in Stat-JR: https://www.bristol.ac.uk/cmm/research/missing-data/
If you have not used Stat-JR before then it is suggested that you work through the quick start guide prior to attending the workshop to ensure that the software is set up correctly. You may also wish to read through the document "Missing Data with Stat-JR".
This course assumes familiarity with multilevel modelling up to the level of module 5 of our LEMMA online course (https://www.bristol.ac.uk/cmm/learning/online-course/course-topics.html) or that you have attended the introduction to multilevel modelling course preceeding it. Participants are encouraged to gain a basic understanding of missing data techniques beforehand, e.g. by accessing the website: http://missingdata.org.uk/. If you are attending this course you will need to bring your own Windows laptop. If you already have Stat-JR then we advise that you install and test this beforehand, otherwise we will provide a cut-down version for use with missing data on USB stick for the duration of the workshop.
Professor Harvey Goldstein, Mr Christopher Charlton
The course starts with registration at 10.15 on Day 1 and finishes at 16:00 on Day 2. Participants are expected to attend the full course.
The course fee includes printed materials, lunch, and morning/afternoon refreshments. The course fee does not include travel and accommodation costs. There will be an optional course meal for participants and workshop instructors for each course on the Tuesday and Thursday evening at an additional cost of £30 per meal.
Introduction to multilevel modelling:
Handling missing data for multilevel modelling:
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 either workshop, please complete and submit the online application form (see below). Please note the closing date for applications is Sunday 18th November.
Submission of the form and its acknowledgement does not guarantee a place on the workshops. We will email you by Wednesday 21st November to tell you whether or not your application has been successful. If you are offered a place on either workshop, it will not be confirmed until you have accepted and paid the relevant fee.
If you have any queries, please email firstname.lastname@example.org.
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Entry (no or almost no prior knowledge)
Introduction to multilevel modelling:
Website and registration
Multilevel Modelling , Handling missing data for multilevel models
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