Thanks for sharing the US report. The presentation of the statistics was, as you said, very clear to follow. The report’s reliance on secondary sources (Smith, 2008) makes it a good case study in using these resources. I’d like to comment on a couple of issues Smith brings up in relation to this study.
The first is definitional. Smith (2008) asks if “Do your definitions match?” (p. 66). I know that you will probably do your thesis data collection in Toronto. This makes the action of clarifying definitional boundaries that may be used in the US and in Canada. Do both jurisdictions define any of these terms in the same way: women, disabilities, Black (even) and so on? The first term ‘women’ is gradually becoming problematic, and exact definitions of ‘disability’ have been issues for a decade. The term ‘Black’ is also not as rigid as it may appear. How does Canada differ in its understanding of these categories? The term ‘Asian’ in the US study, for example, combines both those from the Indian subcontinent and all of China, Japan and the whole of east Asia. Significant differences exist between these populations that may have an impact on your study.
The term ‘Black’ may also be problematic in a diverse multi-cultural area such as Toronto. Do children of mixed parentage count as ‘white’ (for example) or ‘black’? How much does an individual’s self-image affect reporting at the national level? An example from my own experience bears this out. In the 2012 Japan National Census, the form our family had to fill out had no way of reporting our children’s multi-cultural status. The available boxes were: Japanese, Asian, Other. My children have two passports, two languages and cultures, and two very different DNA strands coursing through their identities, yet in the official Japanese eyes, they are either ‘pure’ Japanese or ‘Other’.
The other point I wish to raise is that of the use of ‘underrepresented’. The report maintains that “It offers no endorsement of or recommendations about policies or programs” (National Science Foundation, 2013, p. ii). Yet, within the term ‘underrepresented’ lies an assumption that should be clarified if the report is to be transparent. This assumption rests on the belief that the numbers of individuals in any sphere of activity should be more or less similar to the percentage of that individual’s (whichever kind of) category in the national statistics. So, if the US national population of Black females is 6.4%, there should be around 6.4% Black females in all and any nationwide activity. When the actual numbers differ from that benchmark, research should be conducted to investigate why.
This assumption could be challenged on many grounds, but here I’ll ask only if the definition of, for example, ‘disability’ does or does not include sub-categories that make activities impossible. It seems to me that those with severe mental disabilities, for example, would not be pursuing higher education and, accordingly, should not be used to promote any notion of ‘underrepresentation’. Does it make sense to argue that if the national population of those with a disability is such-and-such a percent, that same number should also be evident in higher education? Without more precise definitions and clearer terminology, much can be missed when using secondary sources. Smith’s (2008) description has much on this. I’ll leave you with the rhetorical question of what exactly does ‘underrepresentation’ actually mean?
National Science Foundation (2013) Women, Minorities and Persons with Disability in Science and Engineering. Retrieved on 28 November 2016 from https://www.nsf.gov/statistics/wmpd/2013/pdf/nsf13304_digest.pdf
Smith, E. (2008). Using secondary data in educational and social research. Maidenhead: Open University Press.