A desirable college is one that most people would want to attend if given the opportunity. This isn't as simple of just reporting which college or university has the most applicants. After all, a lot of students apply to a "dream school" and are accepted, but then don't enroll. Why? And, if you consider retention rates -- that is, whether a student returns after the first year -- you might notice that many students don't return to the same college their second year. Retention rates can say a lot about whether students like a college once they've tried it; it's valuable information for prospective students to consider, but not necessarily by itself.
Calculating a college's desirability, then, requires weighing several factors, not just one or two. Prospective students need to know how many of the people who applied to a college were accepted, enrolled, and decided to stay. They also need to know how many students didn't go to the college or universities that seemed more desireble based on application submissions, and why.
In response to problem of people not understanding what stats to consider when attempting to define a college's desirability, we have devised a desirability metric that anyone can calculate from freely available public data. This metric tells us, in general, which schools would most people want to attend if given the opportunity. Importantly, this metric complements our InfluenceRanking™ because it highlights important differences between schools that people want to attend and schools where previous influencers have worked and studied.
To help students find the most desirable colleges and universities, we considered the number of applications, the retention rate between freshman and sophomore year, the number of enrollments, the undergraduate population (unduplicated headcount), and the number of acceptances. The logarithm is to allow easier comparison between schools that would otherwise have vastly different scores. The exponents ensure that schools with more applicants rank higher and that retention over the typical four-year college experience is taken into account.
The DesirabilityIndex1.0™, or DI1.0, was simply our first algorithm to arrive at a college's desirability score. D1.0 normalized so that 100 was the highest possible score in a given year. For example, in 2016-2017, the highest possible D1.0 score was obtained by using a perfect retention rate (100%), the highest value number of applicants and student population, the maximal number of enrollments, and the highest number of acceptances.
Our DI1.0 scores lack one important ingredient: individual student data. Like many problems in the field of biophysics (Dr. Macosko, the director of this site, is a biophysicist), many problems can be solved by looking at an aggregate of data, but some problems are better solved by studying individual entities, be they individual biomolecules, or individual students. To that end, we obtained surveys from nearly 100,000 students who were heading off to college. We looked at what schools they applied to, which ones accepted them, and which ones they chose. By fitting the exponents in our formula for DI1.0 to this individualized student response data, we obtained DI2.0 scores that better reflect which schools are most desirable.
Fairly weighing the objective data -- application numbers, acceptance, enrollment, and retention, in addition to using our 100,000 student sample to calculate what real students actually do when accepted to the schools, we have a winning formula for scoring how desirable a college or university actually is.