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Data Diving

Universities generate data. Some of it is created for clearly defined uses in the same way that chefs pick specific ingredients to make a fine meal. But some is more like leftovers that end up in the dumpster unless they can be repurposed.

Every university collects and charts census numbers every year. Those overall enrollment graphs are a tasty main course for administrators and trustees – particularly if they are showing movement in an intended direction. But lots of “leftovers” lurk inside. For example, is the number of non-traditional students on the rise? If so, what services might they need in order to complete their degrees? Is a rise in enrollment because of increased scholarships for merit? If so, what might that “discounting” mean for the overall budget? Has the number of international graduate students dropped? If so, will graduate programs that attracted those students remain viable?

Data on under-represented minorities are also collected at most institutions. The primary focus is often on total enrollments of students in these groups with a strong secondary focus on recruiting first-year students who bring diversity to the incoming class. But the “secret sauce” to URM student success often lies in understanding what happens after admission. How well do these students retain, persist, and graduate? If they are not doing as well as majority students, why? The data might offer hints (e.g., less rigorous high school preparation as evidence by ACT scores). But to really get answers to the “why” questions, it is often important to look at finer points of the data (e.g., are math skills the problem) and to talk to the students to hear their “why” story.

Turning leftovers into a gourmet meal isn’t easy. And it isn’t always easy to know how slice and dice data either. For example, socioeconomic factors are not always considered in the URM data. But when they are, and when data on retention, persistence, and graduation of low-income students is examined as both a “what” and a “why” question, new challenges and opportunities can be revealed. For example, an analysis by TorchStar combed the data on low-income students at one university and found that first-year retention was similar to that of majority students whereas graduation rates were much lower. Focused surveys and interviews revealed that for low-income students who do persist, mentors are a critical factor. And the need for mentoring often begins in the second or third year. That’s an insight that a university could make a meal out of.

We’ve often heard administrators say, “we just don’t have the data we need.” But the data often exist even though they may be in the data dumpster. Contact TorchStar if you’d like us to help you find, and slice, and dice the data you need for decision making.

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