By Dana Thomson and Dan Devine
Dana Thomson (Social Stats & Demography) and Daniel Devine (Politics & International Relations) – looked at creating a visual representation of the overlap of datasets and quantitative terminology, across disciplines that apply social statistics, to identify where research gaps are and where there is room for more collaboration.
Our contribution was motivated by our experience in a class together; we both took statistics, but coming from different disciplines (political science and public health), we used different terminology for the same techniques, leading to unnecessary confusion and problems in dealing with the course. Our initial thought was to create some form of ‘dictionary’ or reference tool to iron out these issues, but we soon expanded into the area of survey research. We were interested in what similarities and differences there were in both methodology and data collection, and who (if anyone) has successfully combined the two.
To do so, we created a database of routinely collected surveys at both provincial and national levels, split by our respective disciplines, in three countries: Kenya, Bangladesh and the UK. We collected these over time, so we could identify any change. We found that there was very little overlap regarding both the type of questions asked and their coverage. Politics focused on the national level, whilst public health focused on sub-national health systems. We did however find that both disciplines expanded their coverage over time.
For the methodology part, we sampled 25 peer-reviewed, original research articles from top journals from each discipline. We then used a basic word cloud creator (Wordle) to separate the 50 most common words, removing irrelevant words (like ‘and’) and numbers. We once again found very little overlap, with public health much more technical in its language.
Both of these exercises surprised us. We expected more considerable overlap between two disciplines related both in their methodology and overall aim of improving the human condition. Our attention then turned to examples which best highlight the importance, practicality and success of integrating these two disciplines. In the UK, we found just one: Understanding Society, based at the University of Essex, which has combined rich longitudinal social data with health ‘biomarkers’, designed to bridge the exact cross-over we were trying to address. One issue with this, however, is trying to communicate and publish interdisciplinary work, so we also looked at the Frameworks Institute, which specialises in communicating this research, to see what we could learn from them. Both of these were summarised in the hand outs provided.
Overall, we found a disappointing lack of interdisciplinary in both execution and use. Despite this, there are clear examples of the excellent things that can happen when these two disciplines are bridged, and motivated us both to pursue this further.