The survey was started by 421 people however 23 of these were empty answers and after excluding those who came to the country after turning 16 years of age (n=139) or did not provide a response about their age of arrival (n=13) that number fell to 246, however, two further people did not state which universities they were studying at.
Among the respondents 158 (65%) identified as cis-gendered women, 83 (34%) as cis-gendered men and 3 (1%) as trans or gender non-conforming.
Respondents had a slightly different age profile with White students being more likely to be older, with 30% of White students aged 40 and above and only 18% of BME respondents from that age range (see Appendix 14 figure 4).
There were 188, or 77% of White respondents. This included White, White- Scottish, and White-Other. BME students made up 23% of respondents (57 people) and included all other ethnicities. Of this 31, or 13% of all respondents were from the selected BME backgrounds under the investigation, i.e. Black- British Caribbean, Black-British African, British-Asian Pakistani, British-Asian Bangladeshi (however, there were none among the respondents), Mixed-White- Black African, and Mixed-White and Black Caribbean. Due to low numbers of BME student respondents most statistical analyses have been conducted on
the broad category of BME and “selected BME” (i.e. a subsection of all BME whose ethnicity met interview eligibility criteria) grouped together rather than the individual breakdown of ethnic identities. Mixed White and Asian background BME students were included in the overall BME category (rather than ‘selected BME’) as it was impossible to determine whether they were Indian, Chinese, Pakistani or Bangladeshi mix. See Appendix 14 figure 1 for details.
Overall there was a higher proportion of White respondents who were
undertaking doctoral studies, and only 5 respondents from the selected BME backgrounds studying at this level. For the purposes of the analysis PGT students are those studying for a master’s degree or other PGT qualification, and PGR students are those in a PhD or other professional doctorate
programme (For details see Appendix 14 figure 2)
Compared with the national picture (ECU, 2017) ‘All BME’ respondents at PGT level were overrepresented in this study (25% respondents vs 21% nationally), while ‘all BME’ respondents in PGR courses were underrepresented (13% respondents vs 17% nationally). It has to be noted that the survey was not meant to be representative of the wider HE population and its results are limited to this study only.
The proportions of BME and White students were similar at both types of institutions (modern: University of Knowledge, Education and Warrant versus research-intensive: University of Merit and Labour) for both PGT and PGR courses, with more respondents undertaking PGT courses in modern universities and more PGR respondents being from research-intensive universities (see Appendix 14 figure 3).
In terms of the discipline split, there was no difference between BME and White respondents with roughly 44% of all doing a STEM related course and 56% doing a non-STEM course (see Appendix 14 table 1)
Ascertaining one’s socio-economic class is a difficult task. There is no
agreement among sociologists as to what exactly constitutes and determines it – ranging from economic aspects such as income or wealth to cultural tastes,
behaviours, and capitals or a combination of all of the above (Wakeling, 2016). For the purposes of this research it was done by a series of proxies for
economic and cultural capitals. Self-reported home ownership status of participants’ parents and their professional job status were used as proxies to indicate economic capital. To measure cultural capital proxies in the form of parental education and number of books at home were used.
The survey data reveals that both White and BME respondents had similar levels of (1) parental education (see appendix 14 figure 5) oscillating at about 50% of parents with a university degree and (2) perceived numbers of books at home (see appendix 14 table 2). This can be interpreted as both groups having similar social class profiles. On the other hand, White students seemed to have a higher proportion of parents in professional employment, i.e. requiring a university degree with 58% of White students and 39% of ‘selected BME’ students who answered this question saying at least one of their parent had a professional job (see Appendix 14 figure 6). White students were also more likely to live during their childhood in a property owned by their parents (see Appendix 14 table 2). This can be interpreted as White students having a slightly higher level of economic capital.
4.7.5. Institutional and national documents
Institutional documents, which are widely available on the internet were
collected for the five original interview sites where staff and student interviews took place: University of Benefit, University of Books, University of Confidence, University of Labour, and University of Merit. Prospectuses were collected in September 2016 for academic year 2016/17. Only the prospectus of the University of Benefit was in a paper version, the other prospectuses were in electronic (.pdf) versions. Apart from University of Labour which had separate prospectuses for PGT and PGR that year, all others combined their PGT and PGR offer into one PG prospectus. Other documents included: annual equality and diversity reports for 2016, access agreement for 2014-2015 and access and participation plans for 2019-2020 for the five research-intensive
National documents included the Office for Students’ guidance for the 2019-20 and 2020-21 Access and Participation Plans (OfS, 2019, 2018a) which spell out the latest policy direction of WP, as well as the WP monitoring spending report from the Office for Fair Access (OFFA, 2016)
4.8. Analysis
In this section I outline how the different sources of data were analysed. While the section is organised by investigative tools/type of data collected, the actual analysis within the findings chapters is organised in a way which closely
connects and intertwines the qualitative with the quantitative data, thus, arguably demonstrating the strength and utility of employing the various sources of data in this project. The quantitative data provides additional methods of analysis to the qualitative data driven study in order to extend critical ‘race’ praxis (Solórzano and Yosso, 2015) and address its critique of not exploring diverse sources of data and not linking enough the analysis with the theoretical framework (Baber, 2016).
4.8.1. Linking discourse analysis and critical ‘race’ methodology