Our LEMMA (Learning Environment for Multilevel Methodology and Applications) online multilevel modelling course, contains a set of graduated modules starting from an introduction to quantitative research progressing to multilevel modelling of continuous and binary data.
(see related FAQ, Why do I need to register?)
Some samples of the course topics are on our web site so you can view them before registering: Go to sample PDFs.
The system will be extended to include further modules on advanced multilevel modelling and applications of multilevel modelling to data from different contexts.
"A great course. Just what I needed. Explained everything in detail. The tests were particularly good as they highlighted aspects you thought you understood but hadn't really grasped. A great way to get back into stats. Thanks" (PhD student, 2009)
Whether you are new to statistical modelling or an advanced user, we hope that you will find our materials useful. We recommend you test your current understanding of statistics by taking our prerequisites quiz.
MLwiN, R and Stata. As the MLwiN, R and Stata versions of each practical cover the same material, comparing across the practicals will help users to become familiar with the other package.
We do intend to update many of our materials, in particular the manuals and the LEMMA online learning materials. However this is a fairly large undertaking and it is anticipated that this will not happen for several months. In the meantime we ask users to be aware of these small differences between the latest version of MLwiN and the training materials, and apologise for any confusion resulting from this.
This set of modules is not aimed at the complete beginner in quantitative analysis. We rather expect most users will have some familiarity with many basic ideas and have some prior experience of traditional elementary statistical methods, perhaps up to two variable regression and correlation methods. They will usually also have had some exposure to introductory inference such as the ideas of confidence intervals and hypothesis testing.
However, it may be that someone who has received such training may need their ideas refreshing. It may also be the case that the ideas of statistical uncertainty and inference were not fully understood on the first exposure. Module 1 and Module 2 therefore provide selective introductions to quantitative research and data analysis, with a focus on key topics that will help to contextualise the ultimate task of learning about multilevel models.
If you have a good understanding of multiple regression - including the treatment of categorical explanatory variables and interaction effects - you may wish to skip to Module 4. Confident users of multiple regression, but who have not used MLwiN before, should start with the practical for Module 3.
More advanced modules will be published as they are written.
Modules 1 and 2 are designed to refresh the concepts, definitions and techniques of introductory quantitative data analysis. We expect that users will already have some familiarity with many basic ideas and have some prior experience of traditional elementary statistical analysis. More in-depth treatments of the material covered in Modules 1 and 2 can be found in the following online resources and texts.
We have come across the following sites which we think are useful. Please note, however, that they are not connected with the Centre for Multilevel Modelling.
This is the Research Methods Knowledge Base and is a comprehensive web-based text book that introduces all of the topics in a typical introductory course in social research methods, including quantitative analysis and statistical inference.
CAST includes several computer-based statistics textbooks that uses interactive diagrams to help teach all the statistical concepts. There are three introductory textbooks aimed at different application areas.
New original approaches to statistics for researchers: the examples are taken from exercise and sports science, but the principles apply to all empirical sciences: The site says "If you're new to stats, most of what you read here will be a new view. But even if you have done some stats, there's plenty here that's new. For example, I've discarded most details of computation, in the hope that you will get a better understanding of the concepts. Let's leave the computations to the computers."
Hyper Stat online; an online statistics book with links to other statistics resources on the web.
Concepts and Applications of Inferential Statistics is a free, full-length, and occasionally interactive statistics textbook. It is a companion site of VassarStats, Web Site for Statistical Computation. The materials on this site may be freely used for any non-commercial educational purpose.
The STEPS consortium has developed problem-based modules to support the teaching of Statistics in Biology, Business, Geography and Psychology. The software is freely available to educational institutions, and can be downloaded from the Web site.
Surfstat.australia: an online text in introductory Statistics
If you have a good understanding of multiple regressio - including the treatment of categorical explanatory variables and interaction effects - you may wish to skip to Module 4. If you do not feel fully confident in the application and interpretation of multiple regression, we recommend that you at least answer the quiz questions for Module 3 to test your understanding.
If you are a confident user of multiple regression, but have not used MLwiN before, you should start with the practical for Module 3.
Module 4 and Module 5 cover the same material as the first part of our workshops, although in more detail and using different examples. We therefore strongly recommend working through these modules after attending the workshop to consolidate what you learnt before attempting to analyse your own data.
Further modules will be added as they are developed. These will include additional methods-based materials (e.g. single-level and multilevel logistic regression), as well as applications (e.g. the use of multilevel models in school effectiveness research).
Our workshops assume an understanding and familiarity with the application of multiple regression. If you have not used multiple regression, or feel in need of a refresher, we strongly recommend that you work through Module 3 before the workshop. For those with less experience in statistical analysis, you may additionally find it helpful to study Module 1 and Module 2.
To get the maximum benefit from the workshop, it would be worthwhile reading Module 4 and Module 5 (Concepts) beforehand. If time is short, Lessons 5.1 and 5.2 of Module 5 provide an introduction to multilevel modelling.
You will find quiz questions interspersed throughout the modules to allow you to assess your understanding of the material.
Before starting to work through the materials, we strongly encourage you to test your current understanding of statistics by taking our prerequisites quiz. On the basis of your score in this quiz, a recommendation will be given on which module to start with.
Yes, much of the material can be used without software, or by users of software other than MLwiN.
Modules 1, 2 and 4 do not have practical exercises, and are therefore not tied to any particular software package.
In Concepts, we describe the statistical models using illustrative examples from a range of disciplines; this is done without reference to any software. The Practical component goes through the analysis of a particular data set using MLwiN R and Stata and the modelling techniques described in the concepts component of the module.
No. The materials can be accessed free-of-charge and downloaded by anyone. However, we do require users to register with us first.
During registration, we collect:
We also collect information about your use of and progress through the course, to help us in our research, and to help our funders evaluate the course.
We require you to register sothat we can collect data to help us to conduct and publish research into the learning of multilevel methodology and applications. We hope to study learning trajectories:
...and relate it to our users' statistical and academic background.
However, your personal data will be held and processed in strict confidence by Centre for Multilevel Modelling researchers. We uphold UK data protection laws, and much of our long professional experience has been in the analysis of sensitive educational and health data. Data will be highly aggregated before any publication, so that individual persons cannot be identified. We also collect course users' demographic, academic background, and contact details on behalf of our project funders - the ESRC (through the NCRM - National Centre for Research Methods ). We pass this data onto them so that they can evaluate our course, particularly assessing its uptake. They may also contact you in order to ask you for feedback. You have no obligation to respond to their requests.
More information is available in our Privacy Statement.
Learning materials on our site are free to use under a Creative Commons licence. We will be delighted for you to use these materials for your own (non-commercial) teaching, and ask that you cite us if you do so.
Steele, F. (2008) Module 3: Multiple Regression MLwiN Practical. LEMMA VLE, University of Bristol, Centre for Multilevel Modelling. Accessed at /cmm/lemma.
Do please let us know that you're using our materials (email: firstname.lastname@example.org) - we're keen to support teachers of Multilevel Modeling, and feedback is always welcome!
Please also feel that you can adapt our materials to suit your needs. We've designed in a split between Concepts (generic information) and Practicals (examples using specific software and datasets from particular disciplines), so that there's less that you'll have to change.
Let us know (email: email@example.com) which Practical you intend to adapt, and we'll send you its original Microsoft Word file if required.
We hope that you will create different versions of the Practicals that we can put on the site - publicising your expertise, and drawing the attention of more people to it as the site becomes useful to wider communities. We hope that you'll make versions that use:
To ensure consistency, we may require some changes to your version before we can put it up on the site, and we do not guarantee that we will be able to host your version on our site.
The learning materials are kept in a course management system called Moodle. Moodle is an open source community project, and so has been designed by many people.
The curriculum design and key requirements for the site were by Fiona Steele, supported by colleagues, including: Jon Rasbash, Kelvyn Jones, Harvey Goldstein, Aileen Earle.
The individual learning materials are by the named authors at the start of each Module, including Fiona Steele, Tony Fielding and Rebecca Pillinger, and Jon Rasbash. Materials were edited by Fiona Steele, and sub-edited by Rebecca Pillinger.
Hilary Browne was the web-developer - doing most of the visual design, all of the CSS and HTML hacking, and key parts of the interaction design.
Sacha Brostoff did most of the navigation design, interaction design and usability testing, and a little of the visual design.
Chris Charlton is the system administrator: doing all the php, database, software customisation and web-server magic that put the course on the internet, bent Moodle to our needs, and kept it working.
Unfortunately we do not have resources to answer queries about the content of the materials.
However, go to our help form if you have questions about or problems with using the system, bug reports, or comments on materials.
Or email us on firstname.lastname@example.org to let us know you're teaching with them, or to let us know you would like to put materials on the site.
The materials are very detailed and designed for self-learning. In particular, you can test your understanding of the materials by answering the quiz questions in each lesson of a module. You can retake the quiz as many times as you need to, to understand.
If these materials don't work for you, it may be that brushing up your statistical foundations will help. There are links to prerequisites throughout the course. If this doesn't help, you may need some face-to-face time with someone knowledgeable in multilevel methods. There are workshops several times a year in the UK, and some of them are workshops given by us.
Q: I'm taking the LEMMA Course and until yesterday everything was working perfectly, but today I just couldn't open the MLwiN Datafiles. Every time I try I get this message: "Run-time error '5': Invalid procedure call or argument". I've already install the latest version but it didn't work. I tried several times uninstalling and reinstalling the software, but it didn't work… Any ideas of what can I do? Many thanks in advance.