Higher Ed Data Stories

Two quick additions/clarifications to this:  The definition of full-pays is those students who receive no institutional funds.  EM people don't care where the cash comes from, only the discount.  Second, yes, I know some institutions use endowments to pay for institutional aid.  That percentage is likely very small, although concentrated at a few institutions. Before we begin, here is what this post does not do: It will generally not tell you where you can get low tuition, with a very few exceptions.  And when it does, it won't be at one of "those" colleges. It will not tell you which colleges are likely to close soon, although after the fact, you can probably find a closed college and say, "Aha! Right where I expected it would be!" It will not show you net costs to students. It will not adjust for things like church support, enormous endowments, or the cost of living in that high-priced city where Excellence College or Superior University is located. Got it

One of the intended consequences of test-optional admission policies at some institutions prior to the COVID-19 pandemic was to raise test scores reported to US News and World Report.  It's rare that you would see a proponent of test-optional admission like me admit that, but to deny it would be foolish. Because I worked at DePaul, which was an early adopter of the approach (at least among large universities), I fielded a lot of calls from colleagues who were considering it, some of whom were explicit in their reasons for doing so.  One person I spoke to came right out at the start of the call: She was only calling, she said, because her provost wanted to know how much they could raise scores if they went test-optional. If I sensed or heard that motivation, I advised people against it.  In those days, the vast majority of students took standardized admission tests like the SAT or ACT, but the percentage of students applying without tests was still relatively small; the needle would

I know I've been barking up the tree of " Graduation Rates are inputs, not outputs " for a long time.  And I know no one is listening.  So I do this, just to show you (without the dependent variable) just how unsurprising they are. Here are four views of graduation rates at America's four-year public and private, not-for-profit colleges and universities. And I've put them in four views, with several different ways to look at the data. The first (using the tabs across the top) shows four-, five-, and six-year graduation rates on the left, and "Chance in four" on the right.  In other words, since everyone pretty much thinks they're going to graduate from the college they enroll in as a freshman, what are the chances of graduating in four years, rather than six?  There are some surprises there, as you'll see. On all the visualizations, you can apply filters to limit the colleges you're looking at.  The scroll bar (to move up or down) is on the r

I started this visualization to show how first-year classes at the highly rejective colleges had changed since COVID-19 forced them all to go to a test-optional approach for the Fall of 2021.  But it sort of took on a life of its own after that, as big, beefy data sets often do. The original point was to help discount the conventional wisdom, which is propped up by a limited, old study of a small set of colleges that showed test-optional policies didn't affect diversity.  I did this post last year , after just one year of data made it fairly clear they did at the institutions that had the luxury of selecting and shaping their class.  This year I took it a little farther.  The views, using the tabs across the top, show the same trends (now going to 2022) for Public Land Grants, Public Flagships, the Ivy and Ivy+ Institutions.  In each case, choose one using the control. Note that I had colored the years by national trends: 2018 and 2019 are pre-test optional, gray is COVID, and blue

 Enrollment, as I like to say, is complicated.  But that never stopped anyone from asking a question like, "How does enrollment look?" To help answer, I downloaded IPEDS data of enrollment from 2009 to 2022, breaking it out by full-time and part-time, graduate and undergraduate, and gender, and put it into three different views, below, using the tabs across the top.  As always, you need to be a bit careful jumping to any conclusions about this: There is no easy (or even any hard) way I know of to account for the way Penn State has named and renamed itself over time, and changed the way it reported data, for instance, so anomalies will always show up there.  But for the most part, this information is very accurate.  The first view shows summary data.  This is just to get topline information about trends in US higher education enrollments over time.  Choose the type of enrollment at top right, then filter down to the specific categories you'd like to see.  You cannot break

A new approach to freshman migration, which is always a popular post on Higher Ed Data Stories. If you're a regular reader, you can go right to the visualization and start interacting with it.  And I can't stress enough: You need to use the controls and click away to get the most from these visualizations. If you're new, this post focuses on one of the most interesting data elements in IPEDS: The geographic origins of first-year (freshman) students over time.  My data set includes institutions in the 50 states and DC.  It includes four-year public and four-year, private not-for-profits that participate in Title IV programs; and it includes traditional institutions using the Carnegie classification (Doctoral, Masters, Baccalaureate, and Special Focus Schools in business, engineering, and art/design. Data from other institutions is noisy and often unreliable, or (in the case of colleges in Puerto Rico, American Samoa, and other territories, often shows close to 100% of enroll