Possibly my favorite thing about working in student affairs is the freedom I have to be creative. Granted, a lot of that creativity stems from the need to make magical things happen with no money, but it’s still a perk. Over the past few years, though, I’ve been hearing a lot about data. “Where’s the data? Do we have data to support that? We can’t get institutional support for this initiative without data.” Whoa, when did my fun little idea become an ‘initiative’ that needs data?
Most people who have worked in student affairs for a while are familiar, either in name or in practice, with the concept of big data. Big data is what we think of when we hear higher ed buzzwords like “institutional initiative,” “evidence based practices,” and “predictive analytics.” The purpose of big data is to allow institutions to describe, understand, and anticipate student behavior around such important measures as retention, completion, and allocation of resources. Down here on the front lines, we generally understand that it’s relevant, but don’t always know where it comes from or what it has to do with us in a practical sense.
I’m very interested in big data, and think it has an important place in creating institutions that serve students well, but that’s not where I feel the greatest impact personally. I’m generally more concerned with little data: small numbers of data points drawn from targeted populations, often gathered semi-informally, with a limited research scope, and tied to specific applications. I know my fellow data nerds out there are thinking, “That sounds useless! You’re doing it wrong!” But hear me out. I’ve found little data to be an invaluable tool for me as an academic advisor. The truth is, data has a LOT to do with the fun little micro-initiatives that student affairs is famous for, because our work with students is closely tied to those bigger institutional goals. Interesting things start to happen when you team up the power of big data with the information you probably already have stashed in spreadsheets, files, and event sign-in lists. Working outward from a foundation in applicable student theory, almost any student affairs professional can use little data to focus in on micro-initiatives that actually work.
The main reason that little data is helpful in student affairs is that our ideas and initiatives generally don’t involve every student on campus. We usually have a specific need in mind connected to our areas of expertise. Big data looks at lots and lots of students, and tries to figure out how to use that information to meet everyone’s needs. Student affairs professionals look at a small group of students and try to meet their needs, right now. We’re concerned with a particular characteristic or condition beyond what’s captured in the broad category of “student.” For example, I work primarily with pre-nursing students. My main focus is data that tells me more about how to transform these particular pre-nursing students into strong nursing applicants, and eventually strong nursing students. Their similarities with other students are captured in a multitude of ways by big data, but few of those measures take into account the high likelihood that this cross section of students has shared characteristics related to the condition of being a pre-nursing student. When I run data on my students and my students only, patterns emerge that could easily be buried in the more generalizable parameters of big data. I have a distinct feel on which factors may impact my student population, and my little data controls for those things throughout the collection and analysis process.
There’s a lot going on behind the scenes of a Massive New Student Success Initiative, and much of it is big data so complex that one person with a spreadsheet could never, ever make it all come together into something usable. Little data is different.
Little data plays to the goal of student affairs professionals to change the world one student at a time.
It has some limitations, and in most situations won’t be generalizable to broader populations (at least not right away), but that’s not really the way it’s intended to be used. It’s an advantage for student affairs professionals to recognize that we don’t have to change the entire profession with every project we take on. Little data is meant to address our immediate interests. Institution wide initiatives centered around big problems, like attrition rates or completion, require big solutions with big data to back them up. We can’t just change up the institutional strategy for every group of incoming freshmen. We have to build strategies that we reasonably expect will work for the majority of the people the majority of the time, and stick with them until more big data tells us something else is likely to work better. Little data isn’t meant to fix those big problems, but it can shape the micro-initiatives that impact them. The focus on right-now issues allows fluidity, limiting our expectations to simply creating good outcomes for our target groups and adding to our understanding of how to get similar outcomes in the future. Pulling our scope down to the now may feel short sighted, but in reality, it plays to the qualities unique to the current climate and gives us a context for evaluating new ideas as our needs change.
Little data, free from the pressure of solving institution-wide concerns, can put its’ focus on impacting our responses to very specific, immediate needs. We don’t need years of research to notice that fewer students have scheduled advising appointments this semester or that students are complaining about a lack of campus activities when we know that plenty of activities are being offered. Sometimes big data even drops the problem in our laps; we may get an e-mail telling us that withdrawal rates appear to be an issue with a particular ethnic group, or hear in a meeting that loan default rates are up for the third year in a row. Big data did the work of proving the problem for us, and now we can look at how to reduce the impact of that problem for the students we have right now. Not the students we anticipate having over the next 10 years,the ones we talked with today. We have the flexibility to make changes on the go, so we can use little data to learn and tweak. Little data tells us whether this intervention, with this group, at this time, might be able to move the needle on this particular issue. If we’re wrong, we look for more little data and course-correct in a different direction, a luxury big data often doesn’t have. If it looks like we are on the right track, we can expand activities likely to make a difference for these students. Then, if it continues to work over time, we can use big data to demonstrate that our efforts may have some change-the-world implications worth exploring. We may even demonstrate that, at least for now, our particular students are different from what we would expect based on trends in higher education in general. This saves us the frustration of banging our heads against the wall figuring out why everyone but us seems to be getting results.
Little data gives us an idea of what actions we can take at this moment in time to improve the lives of our students, and how that fits with the bigger picture.
One caveat: the more your promising your data is, the more likely it is that you’ll be asked how you got your numbers, how you know they’re right, and what limitations are assumed by your methods of data collection. These are questions you should be able to answer. It’s often a meticulous process, and you should be conscientious about how you collect and present information. Little data isn’t sloppy data. If anything, you need to be more careful with your data and the assumptions that come from it, because you’re using it to make choices that impact your students directly. However, just like with initiatives backed by big data, having the numbers to support an idea doesn’t create lightning in a bottle. Sometimes you’re right, sometimes you’re wrong. Sometimes you have to just trust your gut and try something new. But little data gives you a starting point, and it’s easier to trust your gut (and to get decision makers to trust it) when you have the numbers to back it up.
I’m not anti-big data. I love it. Big data makes me think, and it reminds me of how important our work is. However, I think understanding the power of project based, specific data to support and evaluate small scale initiatives is largely underestimated in student affairs. We spend a lot of unnecessary time and energy on trial and error because we don’t know how to collect and use information. The small scale and specificity of little data creates a potential remedy for that. The complexity of big data sometimes skews our ability to interpret it properly for our particular needs. Because we’re working with a limited population and compiling small amounts of data, we can often pull the information ourselves from sources we use every day. Little data is for us, by us. We create it, we control it, and we can use it to change our practice, right now.