6 Impressions from AIR 2016
The Association for Institutional Research (AIR 2016) meeting happened recently. It included lots of informative presentations and some useful knowledge. Below is an explanation of who the institutional researchers are and what they do as well as a list of impressions from the meeting.
Institutional research (IR) and institutional researchers. Every university or college in the US and abroad has at least one institutional researcher. Institutional researchers ensure that institutional data (student enrollment, degrees awarded, retention, staff, faculty etc.) are accurately reported to state and federal entities. Because of them (and reporting requirements) we have data for research. Some of the institutional researchers ensure compliance while others work on complex data analyses — predictive analytics related to enrollment management, retention, graduation, etc. In a way an institutional researcher is a data scientist for higher education.
1. Increased professionalization in IR. Institutional researchers — data scientists run complex models and predictive analytics and need advanced graduate work in statistics, or a closely related field, to enter and thrive in IR. The problem is that universities and colleges cannot compete with the industry for these people because they cannot pay high wages. But institutions don’t need to because many of universities already offer programs in statistics or similar fields and they hire graduate students providing them with modest salaries and sometimes tuition waivers so they can cut their teeth with big data prior to moving to where the money is.
2. Big data. There is a strong push in the field for student level data. Currently, student level data is protected through FERPA and many researchers don’t get access to rich data that can meaningfully impact the field. Think about it this way, industry uses individual consumer data for predictive analytics and these data make a huge difference for companies that harness them. In higher education student level data is collected, but not shared. Once the ban is lifted get ready for revolutionary studies that will likely completely change the face and understandings of higher education and challenge many current assumptions and notions.
3. Propensity score matching. Never heard of it before, but I am not a statistician. Apparently it is the new kid on the block and everyone seems to love it.
5. Strategic enrollment management. The market is crowded and everyone wants a piece of the pie. Competition for students is steep and everyone wants the best, most diverse, and most likely to succeed students. Hence, complex stats are run on potential incoming students and anyone building cohorts is highly aware of the risks associated with making mistakes in enrollment management. These mistakes can translate in lost investments (tuition discounting) and decreased future revenues due to decreased retention and graduation rates. These indicators did not use to be such a huge deal, but the College Scorecard brought them to the forefront and everyone feels the sting. These indicators (retention and graduation rates) can factor heavily in student decisions because they are available and in absence of other measurable outcomes they are some of the only things that provide some quantifiable information for college attendance decision making.
6. Focus on undergraduate education. Colleges and universities focus heavily on undergraduate education. Not a bad thing, but graduate education progression and completions are also important. Maybe in a few years they’ll receive some attention, too.
