Rabia Butt and Klara Valentova are our Q-Step interns from the University of Manchester. Q-Step is a £19.5 million programme designed to promote a step-change in quantitative social science training, funded by the Nuffield Foundation and the ESRC. We asked Rabia and Klara to tell us a bit about themselves and their journey to this internship.
I am one of a small cohort of students taking a degree pathway ‘Sociology and Quantitative Methods’ at the University of Manchester.
I have been very enthusiastic about data analysis since the year 2014 when I completed a study exchange programme in the USA. I attended a local high school in Georgia and took a module called AP Statistics. I really enjoyed it, and decided I would like to study statistics even further. This, together with my interest in sociology and especially social inequalities, have led me to study Sociology and Quantitative Methods at the University of Manchester.
In my second year of University, I chose a module about data modelling. I have learned how to use R and developed critical thinking and problem-solving skills, but wanted to enhance these to a professional level. I have been very interested in working with the Census data, and in learning more about deprivation while improving my data skills. Thus, I am very lucky to have been given the opportunity to work at the UK Data Service on calculating Carstairs Deprivation Scores for the UK.
Carstairs index is a summary measure of relative material deprivation that was developed in the 1980s. It comprises four indicators from the Census, which relate to material deprivation (overcrowding, male unemployment, low social class and lack of car ownership).
Some of these variables, however, are a bit outdated, and so for our project, we have decided to include other indicators, which we propose are more up to date.
For instance, we will include total unemployment (female and male combined) in our calculations as there are much more women in the labour force than there were nearly 40 years ago when Carstairs index was created. Also, we take into consideration that lack of car ownership does not automatically imply deprivation as in urban areas not having a car might just be more convenient while in rural areas it is a necessity rather than an indication of wealth.
Over the last two weeks I have learned a lot about the Census, Carstairs scores and deprivation in the UK. Nonetheless, the greatest lesson I have learned is the importance of long, proper research.
We were researching for about two days, and overall found that all the papers did their analyses in the same way. Satisfied with our findings, we moved onto getting the data we thought we needed for the project and started with the analysis.
New questions then arose and we had to do some more researching. Suddenly, we discovered many new research papers, some of which did their analysis differently than us. We began wondering whether our work so far was correct. I started checking all the data we downloaded, did more and more researching, and realised that with this additional research I could do a lot of things in a more efficient way. I therefore regretted not spending more time on the initial research as in the end it would have saved me a fair amount of time.
On the other hand, practice is the best way of learning, and so I learned a great lesson, which will be very valuable to me in the future.
I am very motivated and excited now to learn other new things during the next 8 weeks. I am particularly thrilled that I will have the opportunity to use 3D printing and VR to visualise the findings of the project. These new technologies have an incredible potential and are beginning to be widely used in many job sectors, and so learning how to use them, and how to use them for presenting statistics is an enticing prospect for me.