One thing I didn't understand from the workshop

When I took the data equity primer, there was one question about measuring graduation. From what I recall — I meant to post this sooner; apologies I got distracted and did not, because now I’m fuzzy on details — certain groups of students were dropping out or not graduating at higher rates than others. I remember the proposed change in the question was something like, changing it from “how many students drop out/how many students don’t graduate?” to something like “what are the barriers that stand in the way of these students graduating?”

I remember asking “why not both?” and even after Heather’s very good explanation, I wasn’t quite clear on it. Would someone mind explaining again? Maybe I’ll get it this time!


Hi! Of the two questions, the first one “how many students drop out?” really puts the blame on the students, without acknowledging systemic barriers. So the second one makes this acknowledgement and more clearly identifies what the main issue is: the system is such that sets up barriers based on racism, ableism, xenophobia, etc. Please anyone jump in if I am misunderstanding this question.


Right, and I totally understand the need for the second question.

But it still matters, in the end, how many students graduate, doesn’t it? My question is not whether we should ask one question or the other. I’m wondering why it doesn’t make sense to ask both questions at the same time:

In the system as set up now, what barriers - whether they’re sexist, racist, ableist, xenophobic, or any of the other barriers/bias we can identify or know to exist by talking to the students affected - are preventing students from graduating, and how many more students would graduate if these barriers didn’t exist?

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I think whether something “matters” or not is very context dependent.

If you’re presenting data for an internal workgroup where graduation rates is a metric for a program, and people have the context behind the “why” of graduation rate inequities, then sure, it’s relevant. But if you’re presenting data to an external audience, then a lot of people will see your graduation rate data and pull out negative stereotypes about why inequities exist. (Consciously or unconsciously!) You can try to frame that data differently, and that will help, and yet that doesn’t get at the root issue of the data itself being framed in a way that focuses on individuals. It’s likely that some people will still look at your bar graph of graduation rates and make negative assumptions about BIPOC students, even if you have nice words around it. Now you have a potential of reinforcing bias when you are trying to do the opposite.

So basically, I don’t think anyone is saying “graduation rates are never relevant.” To me it’s more about thinking about why you are pulling data, who you are communicating to and their biases, and what your goal is. And then presenting data accordingly in a way that minimizes harm. The beauty of only focusing on the systems data is it doesn’t let people fall back on their preconceived notions. But it’s also not always possible and other data may still be relevant depending on your audience, goals, etc.

This is all my two cents and I am learning myself so not an expert!


Thank you so much for this fabulous question @jayohday and discussion @vidal and @ralebeau - It’s extremely useful. You’re touching many of the points I’d include in my answer.

I want to amplify the idea that @ralebeau mentioned - which is that in both framing options - the dependent variable or outcome of the analysis is a measure of graduation rates. The difference in the framing is whether we see graduation rates as a measure of student outcomes (things that we want to change about individual students) or as a measure of what the school is producing (things that we want to change about the school).

And, of course, to @jayohday point, there is no problem with asking both questions and as @vidal says, it’s going to depend on the purpose of the research.

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