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Spotlight series

Let's turn the spotlight on our academics!

Find out more about their research and much more below. 

In a way, what really matters to me, more than the direct outputs, is the experience I get through it.

This is a brand new section to the Department of Statistics website. We shall be adding more staff spotlight sections to this very soon so pop back over the next few weeks to find out more on our academics! 

Spotlight on... Professor Fiona Steele

How would you describe your research to a general audience?

My recent research has focused on methods for analysing longitudinal data from surveys that track individuals over time. There are many sources of longitudinal data, including large-scale birth cohort surveys and household panel surveys that follow individuals over many years, and studies that collect ‘high frequency’ data (e.g. daily or weekly) over a shorter time period. The most appropriate study design depends on the research question of interest, and each has its own challenges when it comes to data analysis. A particular issue with any longitudinal design is that data are clustered from having repeated observations on the same individual, which means that standard statistical methods need to be adapted. One methodological challenge that interests me is how to account for household or area effects on individual behaviour when individuals are clustered in households and areas, but household membership and area of residence change over time. Another active area of research is how to model the complex interrelationships between the various types of decisions that people make over time, for example decisions about if and when to have children and when to move house.

The methodological topics I work on are motivated by real-world applications. Most of my research has been in demography, but I have also worked in health, psychology and education, most often with social scientists who provide subject-matter expertise. For me, one of the main attractions of social statistics is the wide range of application areas that we can research and learn about, and the availability of rich datasets.

My most recent project (co-funded by the Economic and Social Research Council and the Engineering and Physical Sciences Research Council) involves developing methods for the analysis of longitudinal data on pairs of individuals (or ‘dyads’). Such data are commonly collected in social research, for example on pairs of members of the same family to assess agreement in perceptions of the quality of their relationship. We will be applying these methods in a study of exchanges of support between parents and their adult children, using UK data from the Understanding Society survey. The research team includes social statisticians from the Department of Statistics and colleagues from the Department of Social Policy at LSE and the Institute for Social and Economic Research at the University of Essex.

How has your work been used by other researchers?

My work on methods for analysing multilevel time-to-event data has been widely used by social scientists and researchers from other disciplines. These data are highly complex because individuals can experience an ‘event’ (e.g. birth or divorce) more than once, they may move between different ‘states’ (e.g. employment and unemployment) over time, and there may be different reasons for moving out of a state (e.g. reasons for leaving a job). These methods have found applications in a range of areas, including sociology (e.g. the evolution of social networks), business (e.g. decisions about the location of multinational enterprises), and veterinary medicine (e.g. transitions between foot conformation, lameness and footrot in lambs).

A key factor in encouraging other researchers to adopt a new approach is to explain it in an accessible way, with examples, and to show how it can be implemented in software. Capacity building has been an important part of my work and can take many forms, such as tutorial articles in applied statistics and social science journals that explain how to apply a particular method (and interpret the results), online training materials, and traditional face-to-face training workshops. For example, as part of the LEMMA project (funded through the ESRC National Centre for Research Methods) I led the development of an extensive online training course in multilevel modelling. The course now has over 20,000 registered users, with a high proportion of students (60%) and many from overseas (75%).

And outside the academic sector?

My research has been used primarily by academic social scientists, and so its impact outside the academic sector is largely indirect through the substantive findings of academics who apply the methods in their own research.

One example of research that has had a more direct impact is a recent study on whether intentions, beliefs and mood following acute coronary syndrome predicts patients’ subsequent attendance at outpatient cardiac rehabilitation (CR). The study, led by healthcare researchers from the Universities of Dundee and Aberdeen, involved structural equation modelling of real-time diary data on cardiac-related beliefs and mood collected using an electronic diary. The findings have influenced work on the redesign of CR through the 2020 vision for CR.

Another way that my research has been taken up outside the academic sector is through the provision of on-line training materials. A large number of users (15%) of users of the LEMMA online course are non-academics, while attendees at face-to-face tend to be academics. It seems that the flexibility of the online format is especially attractive to non-academics. 

Professor Fiona Steele is a Professor in the Department of Statistics. 

 

Spotlight series

Professor Pauline Barrieu 

How would you describe your research in your own words?

I am deeply interested in inter-disciplinary work. For me, research makes sense if I am at the boundary between different disciplines. I have been lucky to work in a wide range of areas, such as economics, finance and more recently the environment. What I particularly appreciate in my research is to see what happens when these areas meet.

Recently, my interest has been about understanding "what is a model". Not necessarily from a statistician’s point of view but more generally. What does a model mean for different fields of research? How do they approach a problem and develop a model to solve it? In this sense, I'm a bit of an outlier, reaching researchers outside of my own field.

A problem encountered by people working on a trans-disciplinary project is that concepts have different meanings depending on your area of expertise. Other scientists will have a different understanding than yours; the vocabulary is not the same for a biologist, an economist or a physicist. Therefore, a lot of time is spent going back to that point, communication, and then you increase your knowledge about other disciplines.

When it works, it challenges the way you think and generates something rich and useful.

How do you export the statistical tools used for the financial market to environmental management?

In a way, looking at the things I have been doing in my research requires thinking about management of risk as well as the decision process. Looking at how to transfer risks from the insurance sector to the financial market and manage them in a better way. Regarding environmental issues, it is also very much about considering the risk you are taking to develop a policy that can lead to an improvement. That is why it goes beyond finance and insurance but also the environment. It is more general.

For a couple of years now, I have been working on a project precisely on this, where I am looking at the very definitions of model, risk and uncertainty in different areas. I have been interviewing researchers from diverse fields, such as statistics, cosmology, biology, chemistry, medical research or drug design. At some point it always comes down to the same idea: to consider a risk and take a decision. 

How do you see research?

For me, research is influenced by the way you are trained. A PhD student will follow a supervisor who might shape his vision of research. Mine had a very sociable approach, and through her I learned how to do research as a sort of nice human experience. Nowadays, a research project can easily start from a conversation with someone I get along with. This is what happened for the project on environmental management; I met this person, an environmental economist, who was very nice and we started this project together.

I don't dissociate the process of doing research from the human experience behind it, and I get a great deal of learning from each other. In a way, what really matters to me, more than the direct outputs, is the experience I get through it.

Professor Pauline Barrieu is the Head of Department of Statistics.  

Professor Chris Skinner

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How would you describe your current research?

A common theme is the statistical methodology of sample surveys and government statistics. I’ve always enjoyed engaging with real world challenges in these areas. One current example is work with the Department of Health on ways to collect sample data to obtain a more accurate statistical basis for the reimbursement of community pharmacies dispensing medicines in the NHS. A second is to look at ways in which the population census can go beyond traditional methods to make use of new data sources such as administrative records. But I’ve also always enjoyed developing the underlying statistical theory and methods associated with such challenges, for example in the area of data privacy.

How has your work in data privacy developed?

It originated decades ago helping a group of social scientists make the case to government to release individual-level data from the GB census. The key obstacle to such release was whether this might breach confidentiality undertakings to people who completed their census forms. My role was to develop a systematic statistical way of assessing the risk of such a breach. Since then, whilst continuing to engage in practice, I’ve developed a theoretical basis for disclosure risk assessment.

More recently, there have been major developments in data privacy in computer science, with much work going on in places like Microsoft and Google, and there has been a need for methods to be integrated within the overarching field of data science. Last autumn I co-organised a six month programme at the Isaac Newton Institute for Mathematical Sciences in Cambridge, to bring together scholars from statistics, computer science and other disciplines to share ideas and develop a more unified theoretical basis for the field. Bridging disciplinary differences has also been an aim of the Journal of Privacy and Confidentiality, founded eight years ago and for which I served as one of the original editors.

And how about the census?

I was asked by the Royal Statistical Society to lead an independent review of the methodological options for the 2011 Census in England and Wales. This led me to look more into new developments in census methodology. Since there is usually only one census per country, international comparisons are essential to understand such developments. I have recently been looking more into this area and am currently preparing a high-profile lecture on international developments in census taking, to present in Washington DC later this year.

What are the big challenges in your field?

The demand for statistical data is ever-growing. But primary data collection faces growing costs and a relentless increase in "non-response" from people declining to take part. This raises key challenges in how to combine alternative types of data sources, including primary sources, administrative records and ‘big data’. Data privacy challenges are also likely to grow, in particular the challenge of how to protect the privacy of data used for research purposes, without damaging the value of the data to researchers.

Professor Chris Skinner is a Professor in the Department of Statistics.