Skip to main content

Educational Attainment by Race and Gender

This is a great example of how Data Visualization helps tell a story.

First, take a look at this table of data and tell me what you see.  I know, right?  Not much stands out of a table of black numbers on a white canvas.

Now look below.  It's pretty much the same data (I did not display SME), but it shows you a pattern you see instantly.  This is the percent of people by age who are enrolled in any school, from pre-school to graduate programs.  On the first view, you see the pattern by age group; each line is a gender/ethnic group (white females, Hispanic males, etc.)  Right away, the story jumps out at you.  In very early years, white students are enrolled at greater rates.  From ages 6-15, things even out, then they split again. (Causality, coincidence, or co-variance with data you don't see?)

The view starts with 1995, but use the slider in the top right corner to scroll through the years.  When you do, you'll see the consistency over time is another story element.  We've made some, but not enough, progress in getting non-white kids to stay in school in greater numbers.

Another point: African-American and Hispanic women are more likely to be in school in their early 30's than other groups, especially recently.

The second tab shows females and males by age group over time.  This time, use the slider to change the age category.  What's the story here? Positive trends for almost all groups; but--sorry guys--women are always a couple steps ahead of you.  As it is in life, so it is in education.

What else jumps out at you? I'd love to hear your thoughts.



Comments

Popular posts from this blog

The Highly Rejective Colleges

If you're not following Akil Bello on Twitter, you should be.  His timeline is filled with great insights about standardized testing, and he takes great effort to point out racism (both subtle and not-so-subtle) in higher education, all while throwing in references to the Knicks and his daughter Enid, making the experience interesting, compelling, and sometimes, fun. Recently, he created the term " highly rejective colleges " as a more apt description for what are otherwise called "highly selective colleges."  As I've said before, a college that admits 15% of applicants really has a rejections office, not an admissions office.  The term appears to have taken off on Twitter, and I hope it will stick. So I took a look at the highly rejectives (really, that's all I'm going to call them from now on) and found some interesting patterns in the data. Take a look:  The 1,132 four-year, private colleges and universities with admissions data in IPEDS are incl

On Rankings, 1911, and Economic Mobility

If you're alive today, you have lived your whole life with college rankings.  Yes, even you.  You may not have knows you were living in the time of college rankings, but indeed, you have been, unless you were born before 1911 (or maybe earlier.)  If you're interested, you can read this Twitter thread from 2020 where I discuss them and include snippets of those 1911 rankings as well as those from 1957, written by Chesley Manly. You can read for yourself, or you can trust me, that in fact the rankings as we know them have been surprisingly consistent over time, and most people would have only minor quibbles with the ratings from 1911.  Perhaps that's because they have always tended to measure the same thing. But what if we did different rankings?  No, not like the Princeton Review where they make an attempt to measure best party school, or best cafeteria food, or worst social life.  Something more quantifiable and concrete, although still, admittedly, a hard thing to get rig

Freshman Migration, 1986 to 2020

(Note: I discovered that in IPEDS, Penn State Main Campus now reports with "The Pennsylvania State University" as one system.  So when you'd look at things over time, Penn State would have data until 2018, and then The Penn....etc would show up in 2020.  I found out Penn State main campus still reports its own data on the website, so I went there, and edited the IPEDS data by hand.  So if you noticed that error, it should be corrected now, but I'm not sure what I'll do in years going forward.) Freshman migration to and from the states is always a favorite visualization of mine, both because I find it a compelling and interesting topic, and because I had a few breakthroughs with calculated variables the first time I tried to do it. If you're a loyal reader, you know what this shows: The number of freshman and their movement between the states.  And if you're a loyal viewer and you use this for your work in your business, please consider supporting the costs