Skip to main content

Your daily dose of "No Kidding"

As a young admissions officer in 1985, I went to my first professional conference, AACRAO, in Cincinnati. I don't remember much about it, but one session is still clear to me. I had chosen a session almost by accident, probably, because it was admissions focused in a conference that was mostly registrars. And fate stepped in.

There was a last minute substitution, and Fred Hargadon filled in for some person whose name is lost to history. At the time, I didn't think I'd stay in admissions long; my personality type is atypical for the profession, and I didn't find a lot to excite me.  But in this session I found someone who could approach the profession, well, professionally; someone who could view admissions in a much larger context than I was used to seeing.  Someone who was more intellectual and conceptual than friendly (although he was both).

I remember a lot of that session, but one thing has stuck with me through all this time.  He said, "In all my years in this profession, I've learned only two things: First, that the block on which  you were born determines where you'll end up in life more than any other factor; and second, if we had to choose the absolute worst time to put someone through the college admissions process, it would be age 17."

It was that first part that hit me.  It still does.  And here is some data that suggests things beyond your control still determine where you end up.  It's from the NCES Digest of Education Statistics, and shows what happened to students who were sophomores in high school in 2002 ten years later.

This is a pretty easy visualization to work with: The bottom bar chart shows the outcomes of the total group.  Then, using the filter at the top right, you can break out the top display by one of several values: Ethnicity (the default), gender, high school GPA, high school type, parental education, parental socioeconomic status, and the student's self-reported aspiration.  You can then see what percentage of each group has attained degrees, some education, or nothing beyond high school.  And of course, you can compare that breakout group to the total.

Use the "Highlight Outcome" function to make any particular level of education stand out.

Of course, the relationships between and among these variables are pretty clear, but the data are still telling: If you're white or Asian, if you're a female, if you were a good student in high school, if you went to a private high school, if your parents went to college, if you parents were wealthier, and if you aspired to a degree, guess what? You were more likely to get a degree.

And of course, while some of these things are a function of birth, others, like your high school GPA and your apsirations, may be heavily influenced by educated, wealthy parents.

Play around a little bit, and if you are able to find one thing on this that surprises you, let me know.


Comments

Popular posts from this blog

Educational Attainment and the Presidential Elections

I've been fascinated for a while by the connection between political leanings and education: The correlation is so strong that I once suggested that perhaps Republicans were so anti-education because, in general, places with a higher percentage of bachelor's degree recipients were more likely to vote for Democrats. The 2024 presidential election puzzled a lot of us in higher education, and perhaps these charts will show you why: We work and probably hang around mostly people with college degrees (or higher).  Our perception is limited. With the 2024 election data just out , I thought I'd take a look at the last three elections and see if the pattern I noticed in 2016 and 2020 held.  Spoiler: It did, mostly. Before you dive into this, a couple of tips: Alaska's data is always reported in a funky way, so just ignore it here.  It's a small state (in population, that is) and it's very red.  It doesn't change the overall trends even if I could figure out how to c...

First-year student (freshman) migration, 2022

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 enro...

Changes in SAT Scores after Test-optional

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 ne...