Interdisciplinarity in the age of data: Teaching data analysis and statistics in the social sciences

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Credit: Nina Albada Jelgersma

We live in the age of data and technology. This phenomena does not just effect our work environment and everyday life, but impacts the learning needs of our graduates who will be facing a world with even with more technology. In this post, Anastasia Ushakova, a Postdoc Tutor in Statistics in the School of Philosophy, Psychology and Language Sciences (PPLS), explores how we can better prepare humanities students to understand statistics, given the difference between humanities and traditional maths and science degrees…

In the past five to ten years, most of the major universities in the UK have started to incorporate statistics training in humanities and social science degrees on both a compulsory and elective basis. This can be seen through initiatives like Q-step (Nuffield Foundation) and many others, including in PPLS here in Edinburgh. Research has shown that anxiety associated with learning maths on these mostly humanities/social science-based degrees may not be uncommon (Macheski et al, 2006; Brush, 1978). So what can we do about this?

The sudden introduction of statistics in non-maths programmes may make students feel that they are not suitable for succeeding in this type of subject. These are often degrees in politics, sociology, psychology, geography, linguistics, and many others.  In this post, I would like to emphasise that the way to approach this subject needs to be changed, and we need to think very carefully about how we can involve our students in this journey while maintaining a more mindful approach of their pre-existing and developing learning habits, for instance, those that they have developed by studying core subjects on their programmes. The aim: to reduce the fear of statistics, or as some may say ‘staDistics’. It is also worth noting that these practices may be most beneficial in undergraduate degrees because it appears that the dislike of research methods classes tends to be more common for younger students compared to more mature learners (BUI and ALEARO, 2011).

One of the obvious suggestions is to switch the focus from maths to intuition; to show that the world can be expressed by certain processes that are fixed or given instead of throwing in a mathematical function. Many students have experienced the very basic ideas of calculation in their everyday life without realising it. By inviting them to think more about these concepts, or giving examples where maths can trick (or surprise) them, may engage them in understanding the concept, and help them spot it in their every-day life, e.g., we could show them how their Netflix or amazon recommendations are calculated using statistics. We could also share the stories of those who contributed to statistical scholarship because students may find that the journeys of applied statisticians are also messy, and solutions are not arrived at instantly!

The key point of teaching statistics in this sense is so the students can use them as a tool to help them become more flexible when doing research in their field; not to be a fully-fledged statistician! Essentially, we want our students to be part of the club where they can challenge established research. To do that, they need to have the right software and tools to feel powerful enough to provide new evidence.

So my main argument is about how the subject can be positioned and the tone in which it is presented. Often, statistics is presented as a hard subject, one which needs lots of hard work, and if you do not do well, it can undermine your abilities as a student. We are certainly in need of more peer support among students and teaching staff. This should be happening along promotion of learning communities by emphasising that the subject is best learnt through engagement with each other, be it your classmate or tutor (Macheski et al, 2006). This is something I often see in my class, where students work sitting on round tables with their laptops, an environment which greatly contrasts the rows of computers in a traditional classroom.

Another thing which is very important, but rarely taken into consideration (given the speed with which we want our students to learn), is the difficulty of the subject and the urge to cover it all at once. We need to provide a respectful environment for students learning at their own pace. Knowledge exchange and engagement across the subject is key, for example, students can discuss statistical results in research papers that they are reading for their non-statistics classes.

There is much to be done to understand how to successfully incorporate quantitative subjects into these degrees in the future. There is clearly a need for integration among both students and colleagues to make this process more rewarding and enjoyable, instead of being scary, isolating, too demanding and unattractive. We live in a challenging age to teach and learn, but an exciting one to experiment and explore together with our students.


Brush, L. R. (1978). A Validation Study of the Mathematics Anxiety Rating Scale (Mars. Educational and Psychological Measurement, 38(2), 485–499.

Bui N.H., Alearo M.A. (2011). Statistics Anxiety and Science Attitudes: Age, Gender, and Ethnicity Factors. College Student Journal, 45(3), 573-585.

Macheski, G. E., Buhrmann, J., Lowney, K. S., & Bush, M. E. L. (2008). Overcoming Student Disengagement and Anxiety in Theory, Methods, and Statistics Courses by Building a Community of Learners. Teaching Sociology, 36(1), 42–48.

Anastasia Ushakova

Anastasia Ushakova is a Postdoc Tutor in Statistics in PPLS. She has completed her PhD in Computational Social Science and has experience of teaching statistics across various social science degrees. Her academic interests span broadly across the area of quantitative methods for social science research and new forms of data. She is passionate about both development and application of statistical methods as well as promoting those to new generation of social science researchers.

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