Quantitative Decision Making

This week I spent a lot of time thinking about how decisions are made. In particular how institutions decide what programs they want to pause, what areas they want to downsize, what positions are no longer necessary. And I came to the conclusion that I normally do when I think about research and types of research that can be done which is that everything is based on quantitative data and ultimately qualitative data means absolutely nothing in higher education, and that is precisely the real reason we are in the situation we are facing right now.

I have had the opportunity to talk to a lot of faculty, instructors, sessionals, and teaching assistants during my time at colleges and universities about the scholarship of teaching and learning (or SoTL). And one of the biggest things that has come up in SoTL discussions is how research ethics boards (REBs) do not understand what SoTL is, do not know how to evaluate a SoTL proposal, and are mainly interested or knowledgeable about quantitative methods. As a result, any one who wants to do research that involves qual methods, or even things that involve ethnographies or auto-ethnographies, the REB boards have no idea how to look at this and give feedback so they will often deny this work.

And this is not just something that exists when it comes to research being done in institutions, but also extends to researchers' experiences with journals and reviewing of articles or even the reviewing of abstracts for conferences. The amount of times I have had conversations with people who have told me their article or abstract was rejected because it was clear that it was a quantitative person who reviewed it who had no background in qual or mixed methods, is not a small amount. I know the quant people are desperately looking for a number in this blog post right now, and will say well this is anecdotal evidence, but you see, this is precisely why we are in the situation we are in right now. 

Decisions are being made about programs and positions purely from an quantitative point of view. How much money do we have, how many students are enrolled, how many instructors do we need, how many positions are available in the community in this particular area when they graduate. All these numbers floating around like that meme of the woman doing trig in her mind. But when you remove the narrative from those numbers, when you remove the impact beyond numbers from those decisions, you create real harm. It is not a coincidence that all the cutting that is around equity and inclusion programs, courses, and departments, are places where there are fewer students, places where there are fewer faculty. Can we stop to think why that is. It is precisely because there is a need for more awareness, a need for more understanding of the kind of work, ideas, support, and community building both within and outside the institution and even country, that these kinds of programs do. 

The quantitative bias is what got us here in the first place, and the quantitative bias is what will ultimately destroy important research labs, unique programs that do the kind of work that is not done anywhere else. If you want an excellent example of this, and a place to put your feelings about how people are not thinking about larger community impact and needs in their decisions, I suggest you take a look at the AWCCA program at George Brown College. I used to teach communications in this program, it is a unique program where the course designs and conversations that are had around course design, take into account the courses that the students take each semester and make sure that those courses speak to each other in the topics and assignments. I have never seen a program with the kind of curriculum mapping done here, ideas build on each other, assignments from one class to the other connect in the ways that they should. There are no silos here, only anti-oppressive frameworks, supporting the need for counsellors and advocates who can work in the interstices of community programming, social work, harm-reduction, outreach and awareness. These are the kinds of programs that they are pausing, or cancelling, because they are small quantitatively, because of numbers, because of the quant and never because of the qual, because they don't know how to even process the qualitative impact and trickle down impact programs like these have. 

So if you are looking to do something about all of this, and you are a purely quantitative researcher, take time to learn about programs like AWCCA, take time to have conversations in your departments about how decisions are being made and who is being excluded in those decisions so that your departments become more monochromatic in vision and belief. Use your love of stats for good and not evil. Also if you want to write George Brown a letter and tell them how they have made a very bad mistake pausing a program that is unique in Canada, with real community impact, reach out and I will give you the contact information you need.