Making sense of those vital statistics
18 April 2012
Behind every quantitative research project, there’s a statistician waiting to make sense of the data, and more than 18,000 users worldwide are working with software developed at Bristol’s Centre for Multilevel Modelling to do it.
There’s no getting away from statistics. They crop up in all aspects of everyday life, says Fiona Steele FBA, OBE, Professor of Social Statistics and Co-Director of the Centre for Multilevel Modelling (CMM). Quantitative data – information gathered to summarise the experiences of large groups of people, make comparisons between groups, and track changes among them over time – are used to inform all social and economic policies, from health and education to housing and work.
Analysing such data is a tricky business, because individual behaviour depends on a number of factors that interact in complex ways. Take Steele’s work on housing demographics, where, as part of a project funded by the Economic and Social Research Council (ESRC) she is building a mathematical model to predict whether someone will move house in a given year. ‘People move for a number of reasons, depending on whether they are single or co-habiting, whether they have children, the ages of those children, and whether they are owner-occupiers or living in rented properties,’ she explains. ‘The challenge is how to represent that complexity in a statistical model.’
As if that weren’t complicated enough, for these models to be truly representative, you have to allow for the fact that people are acting within hierarchical structures. At the lowest level you’ve got the individual, at the second level are households, and at the top level are the areas where those households are clustered. And that’s where multilevel modelling comes in. ‘The attraction of this method in quantitative research is that it allows for the fact that people aren’t operating independently, that their behaviour is influenced by other people and by social groupings,’ says Steele. In CMM, statisticians like Steele produce new multilevel methods for analysing these sorts of data structures, and develop software to apply these methods to research questions.
Among the different types of statistical analysis software used by the international research community, MLwiN is up there with the best, thanks in part to the initial work of Professor Harvey Goldstein and the late Professor Jon Rasbash. These two pioneers of multilevel modelling brought the Centre for Multilevel Modelling (CMM) to Bristol six years ago from the Institute of Education in London.
Since then, CMM researchers have further developed the software and made it more accessible through training workshops and online materials, so that it is now used by colleagues around the world. It is also routinely used by national bodies, such as the Higher Education Funding Council for England, the Department for Education and the Office for National Statistics. Chris Charlton, CMM’s senior software engineer, and Professor Bill Browne continue to develop and maintain MLwiN.
One of the major strengths of multilevel modelling is its versatility: it is used in education, medical science, demography, economics and many other areas. CMM researchers are drawn from the Graduate School of Education, the School of Geographical Sciences and the School of Veterinary Science, and collaborate with colleagues across the University.
- 18,000+ MLwiN users worldwide.
- 2,200+ journal articles cite MLwiN.
- 6,000+ users of CMM’s online multilevel modelling course.
- MLwiN software and its precursors have been around for 22 years.
- 25 years since Goldstein’s seminal paper on multilevel modelling. Goldstein, H. (1986) “Multilevel mixed linear model analysis using iterative generalized least squares”, Biometrika, 73, 43-56.
This article first appeared in the University of Bristol's Autumn 2011 magazine nonesuch by Hilary Brown