Asking more granular EDI questions of its PGRs and staff should be a sector priority. It would enable universities to assess the diversity of their academic populations in the same manner they have done for our undergraduate bodies – but with the addition of a valuable socio-economic lens.
It would equip us more effectively to answer basic questions regarding how far the diversity in our undergraduate community leads through to our PGT, PGR and academic populations, as well as see where ethnicity and gender intersect with socio-economic status and caring responsibilities to contribute to individuals falling out of (or choosing to leave) the “leaky” academic pipeline.
One tool to achieve this is the Diversity and Inclusion Survey (DAISY), a creation of Equality, Diversity and Inclusion in Science and Health (EDIS) and the Wellcome Trust. This toolkit outlines how funders and universities can collect more detailed diversity monitoring data of their staff and PGRs as well as individuals involved in research projects.
DAISY suggests questions regarding socio-economic background and caring responsibilities that nuance or expand upon those already in “equal opportunities”-type application forms that exist in the sector. DAISY asks, for example, whether one has children and/or adult dependents, and how many of each, rather than the usual “yes” or “no” to “do you have caring responsibilities?” Other questions include the occupation of your main household earner when aged 14 (with the option to pick from categories of job type), whether your parents attended university before you were 18, and whether you qualified for free school meals at the age of 14.
EDI data journeys across the sector
As part of an evolving data strategy, UCAS already collects several DAISY data points on their applicants, such as school type and eligibility for free school meals, with the latter data point is gaining traction across the university sector and policy bodies as a meaningful indicator for disadvantage.
Funders are interested in collecting more granular EDI data. The National Institute for Health and Care Research (NIHR), for example, invested around £800 million in the creation of Biomedical Research Centres in the early 2020s. The NIHR encouraged the collection of DAISY data specifically on both the researchers each centre would employ and the individuals they would research upon, in the belief (see theme four of their research inclusion strategy) that a diverse researcher workforce will make medical science more robust.
The diversity monitoring templates attached to recent UKRI funding schemes similarly highlight the sector’s desire for more granular EDI data. UKRI’s Responsive Mode Scheme, for example, requires institutions to benchmark their applicants against a range of protected characteristics, including ethnicity, gender, and disability, set against the percentage of the “researcher population” at the institution holding those characteristics. The direction of travel in the sector is clear.
What can universities do?
Given the data journeys of UCAS and funding bodies, it is sensible and proportionate, therefore, that universities ask more granular EDI questions of their PGRs and their staff. Queen Mary began doing so, using the DAISY toolkit as guide, for its staff and PGRs in October 2024, alongside work to capture similar demographic data in the patient population involved in clinical trials supported by Queen Mary and Barts NHS Health Trust.
While we have excellent diversity in our undergraduate community, we see less in our PGR and staff communities, and embedding more granular data collection into our central HR processes for staff and admissions processes for PGRs allows us to assess (eventually, at least, given adequate disclosure rates) how far the diversity in our undergraduate population leads through to our PGT, PGR and academic population.
Embedding the collection of more granular EDI data into central HR and admissions systems required collaboration across Queen Mary’s Research Culture, EDI, and HR teams, creating new information forms and systems to collect the data while ensuring it could be linked to other datasets. The process was also quickened by a clinical trials unit in our Faculty of Medicine & Dentistry who had piloted the collection of this data already on a smaller scale, providing a proof of concept for our colleagues in HR.
EDI data and the PGR pipeline
Securing the cooperation of our HR and EDI colleagues was made easier thanks to our doctoral college, who had already incorporated the collection of more granular EDI data into an initiative aimed at increasing the representation of Black British students in our PGR community: the STRIDE programme.
Standing for “Summer Training Research Initiative to Support Diversity and Equity”, STRIDE gives our BAME undergraduate students the opportunity to undertake an eight-week paid research project over the summer, alongside a weekly soft skills programme including presentation and leadership training. Although the programme has run annually since 2020 with excellent outcomes (almost 70 per cent of the first cohort successfully applied to funded research programmes), incorporating more granular EDI questions into the application form for the 2024 cohort of 425 applicants highlighted intersectional barriers to postgraduate study faced by our applicants that would have been obscured had we only collected basic EDI data.
Among other insights, 47 per cent of applicants to STRIDE had been eligible at some point for free school meals. This contrasts with our broader undergraduate community, 22 per cent of whom were eligible for free school meals. Some 55 per cent of applicants reported that neither of their parents went to university, and 27 per cent reported that their parents had routine or semi-routine manual jobs. Asking questions beyond the usual suite of EDI questions allows us here to picture more clearly the socio-economic and cultural barriers that intersect with ethnicity to make entry into postgraduate study more difficult for members of underrepresented communities.
The data chimed with internal research we conducted in 2021, where we discovered that many of the key barriers to our undergraduates engaging in postgraduate research were the same as those who were first in family to go to university, namely lack of family understanding of a further degree and lack of understanding regarding the financial benefits of completing a postgraduate research degree.
Collecting more granular EDI data will allow us to understand and support diversity that is intersectional, while enabling more effective assessment of whether Queen Mary is moving in the right direction in terms of making research degrees (and research careers) accessible to traditionally underrepresented communities at our universities. But collecting such data on our STRIDE applicants makes little sense without equivalent data from our PGR and academic community – hence Queen Mary’s broader decision to embed DAISY data collection into its systems.
The potential of DAISY
As Queen Mary’s experience with STRIDE demonstrates, nuancing our collection of EDI data comes with clear potential. Given adequate disclosure rates, collecting more granular EDI data makes possible more effective intersectional analyses of our PGRs and staff across our sector, and helps understand the social mobility of our PGRs and staff with more nuance, leading to a clearer image of the journey that those from less privileged social backgrounds and/or those with caring responsibilities face across our sector.
More broadly, universities will always be crucial catalysts of social mobility, and collecting more granular data on socio-economic background alongside the personal data they already collect – such as gender, ethnicity, religion and other protected characteristics – is a logical and necessary next step.
