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The official blog of @SCHEVResearch at the State Council of Higher Education for Virginia. Discussions about our work, national higher education data policy, and highlights about the data we publish.

 

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Thoughts on Why

by Tod Massa 13. May 2016 22:46

Higher education data collections like SCHEV's have been going on a long time.  Student level collections at SCHEV date back to 1992 similar to their current format. There was a smaller group of collections, consisting of most notably, degrees conferred, prior to that. HEGIS, the progenitor of IPEDS, dates back to 1966 and the Higher Education Act. A significant component of these collections have always been demographics - gender, race/ethnicity, age, citizenship, and other characteristics.

In fact, we've done it so long, I think we often cease to think about why.

There has always been value in knowing who's enrolled and how that compares to the population at large. Achieving equity of access requires such knowledge. Likewise, knowing there are differences in performance by group allows us to at least attempt to improve performance or success of lower-performing groups.

When we start studying behavior based on demographic groups, it is probably a good idea to consider our expectations and understanding of group membership. For example, race/ethnicity is not an immutable characteristic in a data system. It is a representation, generally according to national standards meant to allow the redress of past discrimination, and this representation can change at just about the whim of the student. Further, a student does not have to report the same way at different institutions - even when enrolled at two (or more) institutions simultaneously.

If this is the case, as it is, what are we really measuring? What are our expectations about what we are measuring?

These students are still a pretty small group of the total, but they are present. Attitudes of Millenials toward race/ethnicity aren't exactly locked in stone. Some that I've talked with seem to think they should be inconsistent or perhaps less than accurate to a make point. This creates variance and complication in our data and understanding of any analysis.

All this is to say that I think we should always reflect on what we collect and why.


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