Meet Jennifer Badham, Computational Social Scientist

Tell us about your role within your university:

I am an assistant professor in social data science in the Department of Sociology. What this means is that I am interested in improving the methods we use to understand social problems generally rather than a specific aspect of society.

Within the Department, I am programme leader for two methods oriented postgraduate programmes: the MA Social Research Methods, and the health specialisation of the Master of Data Science.  I also convene two postgraduate modules and am core staff with the Durham Research Methods Centre.

More broadly, I am working to improve the way that computational models are used to make better decisions. While I am part of the Health research group, I am interested in methods that can be used to understand issues in any social science domain. I do a lot of research in agent-based modelling (a type of simulation) and social network analysis. I am also developing methods to more effectively engage lay people in research.

Outside of the university, I have recently taken on a role to help health researchers develop better grant applications that use complexity methods such as agent-based modelling.

What first attracted you to your chosen field of expertise?

I originally trained in mathematics and worked for 15 years in health policy. That combination stimulated an interest in how mathematical and computational approaches can support better decision making in social policy.

What is your favourite subject to teach and why?

My favourite subject to teach is computational social science. Typically, social scientists learn only about statistics in their quantitative methods training. Many social phenomena arise from the interactions and relationships in society rather than from independent individuals. This subject introduces students to four quantitative methods that include contextual factors such social contacts and physical environment, opening up completely new ways to understand social issues.

What can students expect MA Social Research Methods and Master of Data Science?

Both the MA Social Research Methods and Master of Data Science are strongly interdisciplinary; the students have diverse educational backgrounds. This makes for rich discussions.

For MA Social Research Methods, students start by engaging with social issues from different perspectives and learn about the importance of having a large methodological toolbox to grapple with important social issues that seem intractable, such as persistent inequalities or social conflict.

For Master of Data Science (Health), students start by learning Python, R and statistics. This prepares them for the health specific modules and more advanced data science methods in later weeks.

Making progress with complex social issues require as many tools as you can learn, including well established methods such as interviews and surveys and newer methods such as social simulation, machine learning, and visualisation. All these methods have a part of play in changing society for the better. Methods oriented postgraduate training will equip you to ask good questions and work towards good answers.