Kelvyn Jones is Professor of Quantitative Human Geography at the School of Geographical Sciences at the University of Bristol. He has held a Nuffield Social Science Fellowship for investigating multilevel modelling. He teaches research design, quantitative techniques, and the geography of health. His publications include Health, Disease and Society (Routledge), Introduction to Epidemiology (Open University) and articles in Social Science and Medicine, British Medical Journal, British Journal of Political Science, Environment and Planning. He has taught multilevel workshops in Scotland, Canada, the Netherlands, Belgium, Switzerland and at the Essex summer school throughout the 1990's
Populations commonly exhibit complex structure with many levels, so that workers (at level 1) work in particular organizational environments (at level 2); while individuals (1) may 'learn' their health-related behaviour in the context of households (2) and local cultures (3). Similar data structures result from multi-stage sample surveys so that respondents (1) are nested within households (2), in postcode sectors (3), in districts (4), and in regions (5). In many cases, the survey design reflects the population structure, so in a survey of voting intentions the respondents (1) are clustered by constituencies (2). Multilevel models are currently being applied in a growing number of social science research areas including educational and organisational research, epidemiology, voting behaviour, sociology, and geography.
These levels in data are often seen as a convenience in the design that has become a nuisance in the analysis. However, by using multilevel models we can model simultaneously at several levels, gaining the potential for improved estimation valid inference, and a better substantive understanding. In substantive terms, by working simultaneously at the individual and contextual levels, these analytic models begin to reflect the realities of social organisation. By providing estimates of both the average effect of a variable over a number of settings, and the extent to which that effect varies over settings, these models provide a means of 'thick' quantitative description.
The course begins by building on standard single-level models, and we develop the two-level model with continuous predictors and response. Examples of analysis will include house prices varying over districts, and pupil progress varying by school. These models are subsequently extended to cover complex variation, both within and between levels, three-level models, and models with categorical predictors and response (the multilevel logit model). A common pattern of delivery is used throughout the course: graphical examples, verbal equations, algebraic formulation, class-based model interpretation, and practical modelling using the software package MLwiN On completion of the course, participants should be able to recognise a multilevel structure; specify a multilevel model with complex variation at a number of levels; and fit and interpret a range of multilevel models. The course does not cover multilevel analysis of panel-type data, multivariate responses, or survival data, although the course does provide the groundwork for these extensions. This course is appropriate if you are analysing a survey with complex structure, are interested in the importance of contextual questions, or if you need to undertake a quantitative performance review of an organisation.
(Representative text used during the course)
In terms of web-based resources, have a look at Multilevel models project (IOE London) and Multilevel searchable mailbase list
(Texts you should be familiar with before starting the course).
Participants taking this course should have some familiarity with regression modelling and inferential statistics. The aim is not to cover mathematical derivations and statistical theory, but to provide a conceptual framework and hands-on experience with the interactive package MLwin. Students should understand regression intercepts and slopes, standard errors, t-ratios, residuals, and concepts of variances and covariances. In terms of software, previous exposure to a Windows environment is all that is required. Multilevel models cannot currently be fitted using standard packages such as SPSS. Consequently full training will be given in MLwiN.