Four Approaches to Data

During my time at Credo I’ve been on at least 200 different campuses and many of those visits have involved working with institutional data.  As someone who grew up studying numbers, specifically those found on the back of the baseball cards I spent my allowance on, I’ve been intrigued by the different approaches to critical data. The approaches seem to fall into one of several categories to date: 1. Data is a four-letter word! We don’t find this very often, but every once in a while we find a campus where gathering and analyzing data is seen as a drag on intuition and creativity.  As one president used to put it with tongue in cheek:  “Don’t confuse me with the facts!  I already know what we ought to do.”

2. Important?  No doubt.  Organized to gather the right kinds of data?  Not yet. We see this approach more often.  At times it stems from either being short-staffed in general, from the absence of a systematic approach to gathering data, or from not having a key person in the institutional research office.  On campuses where this approach is prevalent there is often some residual guilt about this state of affairs, too, knowing there must be a better way.

3. Data?  We’ve got TONS of it!  In fact, it is sitting right here . . . This is probably the state we find the most campuses in.  They have done a great job of gathering data systematically, participating in many if not all of the alphabet soup student surveys (SSI, NSSE, NSCH, etc.).  They participate faithfully, gather data thoroughly, and then fall short of taking the critical next step of analyzing their results.

4. The Goldilocks Approach. You remember the story of Goldilocks where the porridge and the bed were too hard, too soft, or just right?  Goldilocks campuses strike the right balance between gathering data and making use of it.  As one speaker said at a conference I attended long ago, “the point of this kind of research is not to gather data as an end in itself.  The point is to gather enough data and then act on what it is telling you.” There is a conundrum here, which I would state as follows:  “you can never have enough data, but you can have too much.”  In other words, every study you do can be the catalyst for more studies, which can, in turn, be the catalyst for more studies . . . but if this results in paralysis rather than analysis then the gathering is counterproductive. How do you move towards a state of Goldilocks?  First, determine the most critical data elements to track.  Second, gather that data systematically (and archive it carefully).  Third, make sure your team includes someone with an orientation toward the so what side of data analysis and turn them loose.  Fourth, present your analysis to relevant groups on campus.  Fifth, and perhaps most importantly of all, do something about what you’ve learned!