Saturday, July 8, 2017

Changes in College Attendance by State and Ethnicity, 2005-2015

Note: If you haven't read my post about the 2016 election results and educational attainment, it might be of interest to read that first.  Or later.  Or not at all. Your choice.

This started simply enough: A couple of tables from the Digest of Education Statistics, (tables 302.65 and 302.70) showing the percentage of adults aged 18-24 who were attending a degree-granting college by state and ethnicity in 2005 and 2015.  If you've read this blog enough, you know I have a love/hate relationship with the digest: Great data, but horrible formatting.  The tables are made to be printed on a single 8" x 11" sheet and handed out.  The crucial distinction between data and insight is lost.

Regardless, I reformatted the sheets into something workable for Tableau, and started to look at them. I wasn't having much luck: Some of the states didn't have data on African-American students, for instance, in 2005.  The variable for "Asian/Pacific Islander" was relatively new then, and only a few states had that data available.  Beyond that, I was looking to add some color-coding into the visualization to help make a point, and it wasn't going well.

But I've been fascinated since the election by some of the tweets and writing of Chris Arnade and Sarah Kendzior, who are thinking about what the election results mean in "flyover land."  And my blog post about the election results and attainment has stuck with me, mostly because of the reaction people had to it.

So I colored the states by the 2016 election results, and it got more interesting, as you can perhaps see below.

It's easy for us to look at things like this and chalk it up to "uneducated people voted for Trump." While that may technically be true, leaving it at that makes it too convenient for us in higher education to forget that educational attainment is only partially something you earn; it's also something you're born into.  Some of the ten charts on this post might make that clearer.

This can also, of course, be a post about urban and rural, divides. The division in our country might be as much about opportunity as it is about attainment.  If history tells us anything, it's that people start to rebel when they feel they don't have a chance via any other path.

So as we look at the current reality, the question, as always, remains: What are we doing to change the future?

Thursday, June 29, 2017

What Happens if Federal Money Goes Away?

Strategic planning at universities is always an important process, but it's even more crucial to do correctly these days.  And lots of institutions might be missing a really critical element in scanning the external environment: The extent to which federal financial aid programs contribute to the essential revenue streams that run the enterprise.

This is a fairly simple, if crowded, visualization, showing about 900 private colleges and universities who have good data in IPEDS.  Each dot is a bubble, colored by region, representing a single institution.  Its position along the horizontal axis shows student loans as percentage of core revenues, from left (low) to right (high).  I've included subsidized undergraduate direct loans, unsubsidized undergraduate direct loans, Parent PLUS loans, graduate subsidized, and Graduate PLUS loans in the calculation.  I did not include private loans.

Some of these numbers may seem high, but understand what this says and what it doesn't say: Loans go to pay other things (computers, gasoline, rent, books, food, etc.) so the colleges don't actually see all this money.  But presumably, the funding does make attendance and the paying of tuition possible.

And the IPEDS definition of Core Revenues can be confusing, too, as there are many revenue sources you might not consider.  This is what IPEDS puts into the category of Core Revenues:

  • Tuition and fees revenues (F2D01) 
  • Federal appropriations (F2D02) 
  • State appropriations (F2D03) 
  • Local appropriations (F2D04) 
  • Federal grants and contracts (F2D05) 
  • State grants and contracts (F2D06) 
  • Local grants and contracts (F2D07) 
  • Private gifts, grants, and contracts (F2D08) 
  • Contributions from affiliated entities (F2D09) 
  • Investment return (F2D10) 
  • Sales and services of educational activities (F2D11) 
  • Other revenues (F2D15) 

And if you have investment losses, your core revenues drop.  In other words, it can be misleading. And even if it doesn't, most places don't spend all of their investment returns, so while it shows up as a revenue, it is usually never touched.

Got it?

Second, on the y-axis, is Pell Grant revenue as a function of your Core Revenues.  Same idea as above, but using Pell as the numerator over Core Revenues.

Add these two together, and you'll see what happens to your revenue stream if federal aid goes away.

The bubbles are sized by tuition dependence; the calculation is not standardized, so for the sake of simplicity, I looked just at tuition revenue as a percentage of tuition plus investment income.

If you want to show a single or a group of institutions in context, use the filter.  Just type part of the name and select it.  If you want to look at fewer institutions, choose a region, a state, or reduce the range of core revenues (for instance, type $100,000,000 in the left hand box of the filter, or use the slider, to eliminate very small institutions.)

As always, hover over a bubble for details.

You'll notice some interesting things, I hope.  Mostly, I hope this doesn't frighten you.  Depending on where you work, it can be a bit daunting.

Friday, June 16, 2017

The Discount Dilemma

"You should write something about discount rates."

I hear that a lot these days.  Even though NACUBO does its annual discount study, people still want and crave more.  There is no topic, it seems, as much on the minds of people in universities as discount rate.

But despite my desire to make you, the loyal readers of this blog, happy, there are a lot of reasons I haven't written about discount rates:

  • First, data are old.  It's a long story, but financial reporting (where you learn about financial aid) is reported about a year after the freshman class enrolls.  So the ability to calculate discount is always behind the most current admissions data.  The viz below is for 2014 freshmen for instance, and it's the most recent publicly available.  It's hard to describe to people how much things have changed between 2014 and 2017. (And even harder to figure out why 2016 admissions data are not out yet.)
  • Second, discount is not as important as accountants think it is.  "WHAT?!?" say the accountants! I politely suggest that what you really care about if you're running an institution is net revenue per student, and total net revenue, the cash you use to run the university.  
  • Put it this way: If your tuition is $50,000 and you have a 40% discount rate, you net $30,000 cash per student.  But you're generating less net revenue than your competitor who charges $55,000 in tuition with a 43% discount rate, who nets $31,350 per student.  Still, people insist on comparing disparate institutions on this single accounting measure. Lower discount is not always better.
  • In that same vein, average and total revenue are both important.  If you took only full-pay students in the scenario above, you would average $50,000 with a zero percent discount.  But your numbers of enrolling students would go way down, as would your total; you wouldn't have enough to cover your overhead.  Inversely, you can generate more total revenue by discounting more and enrolling more,  but your costs go up faster than revenue, which is of course not good.
  • Third, most people and stories focus on freshman discount.  If you're a small, tuition-driven, liberal-arts college, that might be meaningful, as freshman may make up 25% or 35% of your revenue. But it's less important at big, complex universities, where we have freshmen, transfer, law, medical, graduate, and other types of enrollment too.  Overall discount is far more important, but usually less discussed.  At some institutions, where educating undergraduates can almost be said to be a side business, the revenue from student tuition is tiny, dwarfed by things like research dollars and endowment return.  Large discounts on tiny fractions don't add up to much.
  • Fourth, net revenue is not the cost to the student.  Pell and state grants come to the university as cash, and look the same as money from a student's pocket, even though they are very different to the student.  You can't tell how much a college costs a student by looking at this data.
  • Fifth, not all discount rates are the same, even at similar institutions with similar tuition rates. You can get to a 50% discount rate by having the class half full-pay and half-full need that you meet; you can also get there by having everyone at 50%.  These are impossible examples, of course, but you get the picture.
  • Finally, discount rate--an accounting measure--is something we used to look at only after the fact, where three decimal points are very satisfying.  It's essentially impossible to manage to that rate in a fast-changing, dynamic environment unless you constrain other outcomes.  As I've said before, when we send out aid awards, we're not planting saplings; we're casting seeds.  You can predict with some precision what percentage of which seeds will take root and grow, but you can't control the wind, the rain, or the temperature, which are all critical to success.
As you look at this, there are a couple of things to consider in addition to the usual caveat about the accuracy of IPEDS.

I calculated discount by taking the Financial Aid Cohort of freshmen (first-time, full-time, degree-seeking) students and multiplying that number by the tuition, then using the total institutional aid.  At most places, institutional aid is not funded, it's just a contra revenue.  But at others, income from restricted endowment funds may actually fund the aid.  Here's a long boring argument about whether this matters or not. For this purpose, I'm taking all aid as unfunded discount.  Thus, discount = Institutional Aid/Gross Tuition Revenue.

Some numbers seem a little crazy.  While a lot of institutions have freshman cohort numbers lower than total freshman enrollment (which makes sense) some have freshman total numbers lower than the aid cohort, which suggests someone in the IR office is counting wrong.  Not my problem.

This does not include all 2,200 four-year, degree-granting private colleges and universities.

Discount only makes sense for private universities, for one thing, which takes us down to about 1,600.  Several hundred of them don't accept freshmen, others have incomplete data, some are rabbinical institutes or schools of theology or massage therapy that aren't of great interest to many. Others have tiny freshman classes.  Thus, this view starts with 675 institutions, and shows five things. The four columns, left-to-right show Calculated freshman discount, Average Net Revenue per Freshman, Endowment Per Freshman, and Percentage of Freshmen with Pell.  The color shows mean SAT CR+M scores, approximated using the 25th and 75th percentiles; gold is low, and purple is high.

Use the filters on the right to limit the view.  For instance, you might only want to look at colleges in your state, or with similar sized freshman classes, or test score averages.  Or perhaps you only care about Doctoral institutions.  Filter to your heart's content.  If you want to sort the columns, just hover over the bottom of the column until you see the small icon appear, like this:

Click through the cycles to sort descending, ascending, or alphabetically.  Click on undo, redo, and reset to, well, you get the picture.

Let me know what you think and what you see here.  That is, if you've gotten this far and I haven't whomped the enthusiasm right out of you.

Tuesday, April 4, 2017

Undergraduate enrollments by ethnicity, 2015

I was doing some research for our own internal discussions, and decided to take it a few more steps to look at enrollment of undergraduate students by ethnicity at about 2,000 four-year, public and private institutions in the US.  (And when you look at the data and wonder why, rest assured, I checked: Miami Dade does offer Bachelor's degrees via online programs.)

It's here, and the first two views are pretty easy to navigate:  Each chart shows a separate ethnicity and lists each institution in descending order.  The first view is by counts, and the second by percentages.  Thus you can see the institution that enrolls either the most Hispanic students, for instance, or the institution with the greatest percentage of Hispanic students, depending on your preference.

If you'd like to focus on a single state, just public or private, or colleges of a certain size range, use the filters at the top.  You can always reset the views using the control at the bottom.

The third view allows more customization.  Each point represents an institution, arrayed on the x- and y-axis.  But you can control what values the axes show: For instance, percentage White on the x-axis, and percentage Asian/Pacific Islander on the y-axis.  The points are colored by control: Orange is for private institutions, purple for public. Again, you can limit by undergraduate enrollment or by state, if you'd like.  But this view has the advantage of choosing a highlight institution: Use the highlight box to put a university of interest in context.  Type part of the name, and select it, and it will show up all by itself.

I hope this is helpful for use with students who are interested in thinking about and comparing colleges and universities by enrollment profile.  And if you're interested in seeing how an ecologist might look at enrollment diversity, check out this piece I wrote for Academic Impressions last fall.

Friday, March 10, 2017

Is This Why Democrats Support Education Funding?

This post started off simply enough: I found some cool data on changes in educational attainment over time. I was going to take a look at how far we'd come as a nation in the last 40 years (even though I had already published this), and show where the biggest gains were.

It wasn't very compelling, at least at first.

Then, I decided to get ambitious (my wife was in her evening class, so I had the night free), and wondered if there were any interesting connections between changes in educational attainment and voting patterns in 2016.  I found a data set with election results by county, (a handful of counties are missing) and merged it in. And thus, this.

Before I start, there are a few points to make about the data.  First, the definitions changed slightly over time.  For instance, in 1970, the field is labeled "College degree," while in 2010, it's labeled "Four or more years of college."  Not the same thing, but we'll have to go with it for now.  Also, 2010 is not really 2010; it's the data from the five year American Community Survey of the Census Bureau, but there's no reason to believe it's not as accurate as the census itself.  In fact, the ACS is used to test the accuracy of the census.

So, onward.

Using the tabs across the top of the visualization:

First, a scattergram, plotting attainment in 1970 and 2010.  The regression line suggests that attainment has essentially doubled in 40 years; those bubbles (counties) above it have done better; those below worse.  Bubbles are sized by votes in the county in the 2016 presidential election.  If you want to look at just one candidate, use the highlighter function.

Note that the counties that went for Clinton tend to be larger (more urban), with higher levels of attainment (moving toward the top right) and more above the line.  Counties that went for Trump tend to be the opposite.  And of course, there are many exceptions (Johnson County, Kansas; Apache County, Arizona), and clearly the binary blue/red can be misleading; some victories are by a point, some by 15 points or more.  Finally, it's almost certain that much of the change in attainment is due to people moving in and out; not everyone lives where they were born. But it's still interesting.

Second, the bar chart (I know from experience that many people won't click on the second tab. Please. You will be glad you did).  The x-axis is broken into college degree attainment. For instance, the long bars in the center show counties where 30--34.99% of adults have a college degree.  You can see how many votes these counties cast for Trump, and how many for Clinton.  I double checked this; it is perhaps the best story I've ever told with one chart.  And although the left end changes as you select single states (using the filter at the top), the right end is fairly stable.

Finally, the last chart just shows three variables: 1970 and 2010 college-degree attainment, and the change over time.  See the box to the right of the chart if you want to sort the data.

Admittedly, this election was a strange one, so perhaps there are no lessons to be learned.  But over the past few decades, Republicans have been fairly staunch opponents of increased educational funding, and you have to wonder if this doesn't explain why; people who lived in areas with higher levels of education voted for Democrats in the last election.

Fifty years ago, the Republicans were the party of the college-educated, white collar classes; the Democrats the blue collar, working-class, high school educated citizens.  That's all changed, if 2016 is any indication.

Agree? Disagree? Let me know in the comments below.

Friday, March 3, 2017

2016 Freshman Admissions Data

Note: I've just discovered that although this data set is labeled 2016, it is for the 2015-2016 data year; thus, this is Fall, 2015 admissions data, not Fall 2016 as I had thought.)

This always proves to be a popular post: The 2016 Admissions Data summary.

Here you'll find ten views, showing test scores, admit rates, need data, and international student information (which should only be used as a guide, as you'll see.)

Use the gray boxes and/or arrows across the top to navigate this information, and the filters to limit the views.

Note: This data is "as reported" to Peterson's and is presented as is.  If it's wrong or your college is missing, it's almost certainly a reporting error; most institutions left at least some fields blank.

It comes from the Peterson's Undergraduate database and the Peterson's Undergraduate Financial Aid database, both copyright 2016 by Peterson's-Nelnet.  The data here are used with permission of Peterson's.

Wednesday, February 1, 2017

Welcome to the Hen House, Mr. Fox

Jerry Falwell, Jr., President of Liberty University, has just indicated that he will head a new task force to examine the Department of Education's policies on colleges and universities: Things like, "overreaching regulation” and micromanagement by the department in areas like accreditation and policies that affect colleges’ student-recruiting behavior, like the new “borrower defense to repayment” regulations," according to this report in the Chronicle of Higher Education (subscription required, but if you're in higher ed and you're not subscribing, you should. I've not been paid to endorse the Chronicle. Or anything, for that matter.)

Thousands of people on Twitter and in other social media have already pointed out why this is a bad idea, especially coming on what appears to be the approval of Besty De Vos as Secretary of Education.  But if you want to see how far Liberty has taken recruitment, even under the current regulations that attempt to make admissions and recruiting more ethical, I challenge you to fill out the inquiry form on their website.  Then count to ten, and I'm guessing you'll soon be connected to one of their friendly, helpful, sales agents who will tell you how you, too, can become a champion for Christ, and how you can use federal dollars to do so.

Here is how much federal aid Liberty students (and those from every other university receiving your tax dollars) received in 2015-16 (a few of the programs are 2014-2015, but they don't change much.) Interact to your heart's content.

And feel free to share this with your elected officials.

Tuesday, January 31, 2017

What kind of jobs can English Majors Get?

What kind of jobs can I get if I major in English? (Lots) Do I have to major in science to go to medical school? (No) Do actors have to go to a Theater program? (No).

All these sound like conventional wisdom, but now, thanks to my friends at Human Capital Research Corporation, we have some better answers.  The data set they put together is based on The American Community Survey (ACS) of the Census Bureau, a small but statistically significant sample of the US Population.  It asks questions that include occupation and college major (for those who are working, and for those who have a bachelor's degree).  The data below contains over 3 million individual responses to these questions (for people in the labor force between the ages of 25 and 60 with a bachelor's degree).

One the first dashboard (using the tabs across the top), you see two views.  On the blue chart on the left, choose a major (cluster) at the top.  The chart below will show you the professions (also clustered) of people with a bachelor's degree in that area.  Hover over a square for details, including the number and the percentage of the total.  Multiply by about 20 to convert the sample to the total.

One the red chart, choose the profession, and see the majors of the people working in that area.

Most engineers majored in engineering; most nurses in nursing, most teachers in education, and most accountants in business.  But beyond that, you get a rich sense of the wide range of careers open to people with almost any degree.

On the second tab, look at the majors on the left, and see how people are distributed by going across the row. Look for larger, blue bubbles to see clusters: 37% of people with a degree in library science, for instance, work as a librarian; 29% of architecture majors are architects.  The rows total 100%. Unfortunately, the number of professions makes labeling the professions impossible, except in the box that pops up when you hover.

Then, on the third tab, the view is the same, but the columns total 100%.  So you see the majors of people in professions.

On the last two views, the story is not the large bubbles, I think, although the add to understanding; the story is the small bubbles: People from all majors doing all jobs.

And a word of caution, of course: I defaulted the first two views to biology and medicine, and the tendency will be to conclude that you must be a science major to go to medical school.  In fact, this is likely driven by the fact that the vast majority of applicants to medical school major in the sciences.

What else do you see here? What surprised you?  Let me know in the comments below.

Wednesday, January 11, 2017

NY City Public Schools, and what they might tell us about the SAT

Recently, I received a message from Akil Bello who pointed out a data visualization he had seen.  It was originally posted to Reddit, but later was edited to eliminate the red-green barrier that people with color-blindness face.  The story was here, using a more suitable blue-red scheme.

There's nothing really wrong with visualizing test scores, of course.  I do it all the time.  But many of the comments on Reddit suggest that somehow the tests have real meaning, as a single variable devoid of any context.  I don't think that's a good way to analyze data.

So I went to the NY City Department of Education to see what I can find.  There is a lot of good stuff there, so I pulled some of it down and began taking a look at it.  Here's what I found.

On the first chart, I wanted to see if the SAT could be described as an outcome of other variables, so I put the average SAT score on the y-axis, and began with a simple measure: Eighth grade math and English scores on the x-axis. Hover over the regression line, and you'll see an r-squared of about .90.

Scientists would use the term "winner, winner, chicken dinner" when getting results like this.  It means, for all intents and purposes, that if you know a high school's mean 8th grade achievement scores, you can predict their SAT scores four years later with amazing accuracy.  And--here's the interesting thing--the equation holds for virtually every single school.  There are few outliers.

Ponder that.

But critics of the SAT also say that the scores are reflective of other things, too; an accumulation of social capital, for instance.  So use the control at the bottom to change the value on the x-axis.  Try economic need index, or percentage of students in temporary housing, or percentage of the student body that are White or Asian. The line may go up (positive correlation) or down (negative) but you'll always see the schools with the highest scores tend to have the characteristics you'd expect.

Jump to the second tab.  This is more a response to the Reddit post: The top map shows the ZIP codes and a bubble, indicating the number of schools in that ZIP.  The bottom map shows every school arrayed on two poverty scales: Economic Index and Percent in Temporary Housing.  The color shows the mean SAT score in the school (Critical Reading plus Math, on a 1600-point scale.)  Purple dots represent higher scores.

Use the ZIP highlighter, and you'll see the top map show only that bubble, and the bottom will show the schools in it.

Got the lesson?  Good.  Now, think about why the colleges with high median test scores a) have them, and b) tend to produce students with high GRE and MCAT and LSAT scores,  and c) point to excellent outcomes for their students.

And let me know what you think.

Wednesday, January 4, 2017

The Outlook in Illinois

Much of what I post here is slightly modified from what I use at work, and this is no exception.  Here at DePaul (like most universities) the biggest single slice of enrollment comes from our own state, and it's important to know what's going to be happening to the student markets in the future.

So I downloaded data from The Illinois State Board of Education showing enrollments for two years: 2010--2011 and 2015-2016 to see how things have changed over time, and to get a glimpse of the future.  This is a more granular look than the WICHE data I visualized recently, but it's also not actual projections going forward, but rather just numbers; projections require a lot of time and mathematics skills, neither of which I have.  I would have liked to gone deeper and farther with this, but the data are messy, and even things like School District IDs have changed over time.

There are four views using the tabs across the top: First by region, then county-by-county, and then a scattergram showing each county by both percent change and numeric change over time.  On each, make a choice at the of the page to change the data displayed: You can look at total pre-K through 12 enrollment, if you like (the default view) of you can change to show grade-level enrollments, or by ethnicity or low-income status.

Finally, the last tab shows individual schools.  You can type part of your school's name in the drop down box to start filtering, but be sure you find the county as well as the school.  If you're going to be looking up "Lincoln," you've got a lot of work to do!  Also, some schools have their name listed slightly differently in different years, and if your school is one of them, you won't get two years of data showing.

Please note: The data are not granular, so you can't combine variables (for instance, low-income students in 8th grade.)  And, I've excluded small numbers from the analysis (students in juvenile detention centers, or public school students being education at other sites.)

But it's still interesting, I think, especially if you drill down a bit using the filter at the top.

What do you see? Leave a note in the comments.

Friday, December 16, 2016

Medical, Law, and Dental Degrees, 1955-56 to 2013-14

You can look at a lot of places on this blog to find the story of women and the increases in educational attainment over time, but perhaps none is so compelling as this one.  It was very rare for women to have college degrees in the 1940's and 1950's, but even rarer to find doctors, lawyers, and dentists who were women.

As you'll see below, that all changed in the late 1960's and early 1970's.  What happened? It's probably a lot of things, but you could probably do worse than to point to birth control as a major contributing factor.

There are four views of the data from the Digest of Educational Statistics:

View 1 shows all degrees over time to men and women; the top via stacked bars, and the bottom using line charts.  The top chart shows the dramatic increase in degrees to women; the bottom shows that in 1955-56, almost all degrees (blue line) were awarded to men (purple line.)

View 2 shows the same data, presented a different way.  On the top chart, you see total degrees awarded, broken out by degree type.  Use the filter to limit the view to men or women.  On the bottom, degrees are awarded by percent of total: In the early 1960's, for instance, 99.62 percent of dental degrees were awarded to men.  By this decade, the totals had virtually evened out.

View 3 shows percentage change since 1955-56 for all degrees: Filter to law, dentistry, or medicine if  you wish.  Any way you look at the charts, the data are astonishing.  Especially interesting is dentistry, where there are actually fewer men graduating today than half a century ago.

And finally, View 4 shows institutions: There are now 70% more medical colleges, 60% more law schools, and 36% more dental schools than at the start of the analysis.  This latter number is interesting, however; while law schools and medical schools were at record numbers in 2013-2014, dental programs peaked in 1983-84.

Once again, women, given a more equal shot at education, outpace men by a considerable margin.

What do you see here? Leave a comment below.

Thursday, December 8, 2016

A Fresh Look at the New WICHE Data

Note: You should view this on a tablet or desktop.

The Western Interstate Commission for Higher Education (WICHE) has just published the 9th version of "Knocking on the Door," a look at demographic projections of high school graduates in the US.  And several organizations have already published interesting views of the data, like this one on the WICHE site and this one on Hechinger Report.

As I make my case for more self-service BI, these are great examples of what I call the 80/80 rule: Eighty percent of what an analyst will give you is not what you need as a practitioner; and 80% of your questions won't be answered when someone else does the analysis for you.  So I took the data (and allow me to complain a little bit about putting data for 50 states and DC in 51 worksheets in an Excel workbook, WICHE) and spent a lot of time restructuring it for analysis.  Then I started asking my questions, and came up with 6 views, in an attempt to provide practitioners maximum flexibility. On most of these, you can change start dates, end dates, states, regions, and ethnicity. Even after a couple of hours on this, I could have come up with 60 views, but my spare time is, of course, limited.


I started with two high level questions.  The first is: What's going on at a macro level?  And thus this. The gray bars show total numbers of public high school graduates between 2000-01 and 2031-32; the lines break out those numbers by ethnicity.   So when your trustees ask about these numbers you can (but I wouldn't) say, "It looks like 2017 will be the low water point for at least eight years.  It's good news going forward."

Because if I were you, and before I talk to your trustees,  I'd want to know why lies underneath the macro trends, and thus my second question: How the changes look related to different ethnic groups: Take a look instead at the colored lines, showing break outs by ethnicity:  As I've written before, ethnicity matters because ethnicity and income and parental attainment all go together. The two groups that attend college at the greatest rates are showing shrinking populations (White students) or modest changes in numbers (Asians, growing at less than 20,000 by 2024.)

But wait.  Suppose you're in New York, or California, or Florida.  In all probability, your enrollment demographics are shaped more by what happens 500 miles from campus than 3,000. And Illinois? We've pretty much seen the best we're going to see until after I retire. So interact, and use those filters to get the view you want.

Tidbit: The population of White graduates peaked almost ten years ago.  And Hispanic populations will peak in 2024 after a long, steady, impressive increase since 2000.

View 1: National Overview

Issues of Control: 

It never fails: When I do the data on public high school graduates, someone asks about private schools. WICHE has included that data this year, and it's, well, sort of boring. The numbers are falling almost everywhere, and as a percent of the whole, are not keeping pace.  As you might expect, private school enrollments are a bigger thing in New England than elsewhere. (These data are not available by ethnicity.)

Tidbit: When a super-selective college brags that 60% of its students are from public schools, you can now understand that what they're really saying is private high school graduates are over-represented in their student body by a factor of five.

View 2: Public and Private

Digging down: The last four views (using the tabs across the top)

Now it gets a little more fun: Four ways to look at change over time.  In my business, knowing what's coming is very important; we have about an 18-year window on our markets, and no one's going to allow you to claim you were surprised.

I've become a fan of Hex Maps, to allow visualization of data on a choropleth map. It's never been easy to color-code on traditional state maps because Rhode Island and Delaware are so small, and Alaska and Hawaii are so far out there.

This map view allows you to see change over time (any two years you choose) by state; to show it for all public high school graduates, or just certain ethnicities, and to show numeric change or percent change.  I'm guessing you'll want to use this one a lot in strategic planning groups; it's my favorite.

Tidbit: Michigan. Notice how it stands out; that's a surprise to me.

Click to the next tab on the top, and you get the view for those who like bar charts.  You can still specify change between any two years, but this data is broken out by ethnicity. You can specify region or single state.

Tidbit: I was expecting bigger drops in New England, but no matter which years I focus on, the drop is smaller than I expected.  You?

The third tab is similar, but line charts over time.  It shows all ethnicities, allows  you to specify the years, and shows both numeric and percentage change from the first year chosen (be careful; these are not numbers, but rather changes).

Tidbit: Although Texas and California have seen dramatic increases in numbers of Hispanic graduates, it's Asian students in both states who will grow at the fastest rate between now and 2031.

Finally, the final view shows regions of the US, and the composition of the high school graduating class by ethnicity.

Tidbit: Pull the slider to show the changes over long periods.  Note how fast some regions (Far West, Southwest) change, and how slowly others don't (Plains, Rockies.)  Those last two regions will still be more White in 2032 than the whole US was in 2001.  And notice the relative stability of African-American high school graduates over time, as a percentage of the total.

Views 3-6: Changes over time

If you use Tableau, and  you want to download this workbook in its entirety, I can share it with you; I'll also be happy to send you the data in a much more accessible database format suitable for your own analysis.

I'd love to hear what you see on these visualizations or on your own analysis.  Leave a comment below.

Wednesday, November 16, 2016

Undergraduate Institutions of Doctoral Recipients, 2014

One of the most popular posts on this blog has been this one, where I showed the baccalaureate college of the nation's 2011 doctoral recipients.

This is an update to that, using 2014 data from the NSF.

It's pretty simple: There are three views here.

On the first view, you can see the undergraduate college of all doctoral recipients in 2014.  The view starts with known US institutions only, but you can add in foreign or unknown institutions if you'd like.  You can also look at a single state, the degrees awarded at the institution (for instance, if you have a student who really wants a liberal arts college, choose "Bachelor's-granting institutions"). Finally, choose the broad category of the doctorate, if you'd like, or even the specific program.  Note that the filters cascade: If you choose "Life Sciences" under Broad Category, you won't be able to find "Economics" under Specific Program.

On the second view (using tabs across the top), you can look at a single institution and find out how many graduates received a doctorate in 2014, by broad area.  Note that it doesn't matter if the person received the Bachelor's degree in 2010 or 1968: It's just everyone who earned a doctorate in 2014.

Finally, the last view shows institutions awarding doctorates, regardless of where the student originated.  You can see foreign and unknown institutions, US institutions, or all institutions as the baccalaureate college.

I hope this is helpful as counselors work with students on their plans.

Wednesday, September 28, 2016

Test score distributions, 2014

We tend to think a lot about a college's average test scores, despite the many ways colleges can and do manipulate them for their own benefit.  After my last post on the relatively low number of students who enroll in the most selective institutions, someone asked if I could do the same for test scores.  So here they are.

I've calculated very close mean ACT Composite and SAT CR+M means by taking the midpoint of the 25th and 75th percentiles.  They're almost certainly not perfectly accurate, but are very close, in all probability.  Then I've broken up enrollment to show where students attend college.

The first view is based on the earlier visualization; the second is a scatter showing both the ACT and SAT averages.  The first has just three filters; the second has more, plus a "Color By" parameter that allows you to color the colleges by one of several factors.

I hope this helps people think about and put score ranges in some context.

(Note: IPEDS does not collect test scores from test-optional colleges, or those that are open admissions.)

Monday, September 26, 2016

All the fuss, updated

One of the very first posts I did on this blog was showing just how many "Uber Selective" colleges and universities there are (or aren't), and how many students they enrolled (or didn't.)

I used it last week at a presentation at NACAC, and several people asked me if I had an update on it, so as soon as I got home, I pulled down the data and started visualizing it.  It's below, and it should be self-explanatory: Of the 1,943 four-year institutions shown, only 18 admit less than 13% of freshman applicants.  These institutions (blue bars) enroll just 82,000 students (under 15,000 of whom are African-American, Hispanic, or Native American), and only about 18,000 freshmen.  Yet they get a relatively large share of the press and attention whenever the discussion turns to college admission.

This has limited interactivity: You can choose region, public or private, or Carnegie group.

And of most importance: This is but a sliver of American higher education; for instance, 9% of all college students enrolled in the US attend a community college in California; and another 4% at community colleges in Texas.  Keep that in mind as you look at this data.

Tuesday, September 20, 2016

Who's Going to NACAC?

One of the things I hope to show people on this blog is that data is a lot more fun and interesting when you actually do something with it, rather than just present it in a spreadsheet. Here's a good example.

This week, over 6,000 people who work in or around college admissions will converge on Columbus, Ohio for the NACAC Conference.  (Yes, Oktoberfest is also in Columbus this weekend, and based on my informal discussions, there may be some overlap.)  NACAC puts its attendees in a table on its website for anyone to use.

But it's just data: What does a simple spreadsheet have the power to tell us?  Maybe more than you think.  Yesterday, I put the information in a visualization (first page is set up for mobile but autosized) designed to help people find other attendees.  As a side effort, I put up a chart of the most common first names of attendees, and it proved to be very popular. So last night I did a little more, and looked at most common first, and last names, as well as city, state, country, and organization.  They're below, and I think they say a lot about our profession.  What the information says is up to you to decide.

If you want to interact, click on a first name, and the other views update.  See? Interactivity can be fun too.

A note about the data: I did only minimal cleaning on it; when 6,000 people enter data on a form, there are bound to be errors.  Chicago, for instance, is not in Bosnia-Herzegovina. And I'm pretty sure Beijing is in China.  I did not clean up names, so if you really think your first name is "Mr. Daniel" you miss out on a chance to be included with the other Daniels. And Daniel is Daniel, not Dan, so variations are not grouped together.

Have fun.  And tell me what you think the data says.

Thursday, August 18, 2016

Tuition and Income in the States

Whoa, you might say as you look at this. It's way too funky for me. That's OK; I'm going to show you a new feature in the data visualization tool, Tableau, that I use that will make this all make sense. Hang on.

I wondered: Do states with higher median income levels charge more for tuition?  So I began to explore.

On each dashboard, median family income is displayed on the top chart, and college tuition on the bottom.  The view starts with four-year publics, but you can change it using the filter. The first dashboard shows only the rank of the states, from 1 to 5, with 1 being the high value in each.

If you can't make sense of it, don't worry: Use the little box in the upper right hand corner to select any single state, and that state's data will be instantly highlighted on both the income and the tuition chart.  You can see where a state stands on both measures.

The second dashboard (using the tabs across the top) shows the actual inflation-adjusted values (that is, $57,894 dollars in median family income, or $11,592 of tuition, both set to 2013), but the ranks are also displayed.  Use the state highlighter the same way, and hover over the dot for details. Note on this income chart I've broken one of my cardinal rules by not starting the y-axis at zero, for the sake of clarity.

You can get a sort of affordability index by looking at income ranks in comparison to tuition ranks, and you can see trends in both over time by state.

What do you notice here?

OK.  So maybe that's too funky.  Here's the same view, colored by red (high rank) to blue (low rank). If you like the original, it's below.

Wednesday, August 17, 2016

How Many Colleges Are There, Anyway?

A note in response to some questions from IPEDS geeks and others:  My data selection was from 2014 IPEDS data.  I used Title IV participating, US only, all sectors except administrative units.  That resulted in 7,018 institutions.  My visualization shows 6,876 because there were 142 institutions with absolutely no data reported.  I should have defined in my original post.

Also, the selectivity bands are not defined: Cut points are at less than 15%,, 25%, 40%, 60%, and 75%.  All others are "Not selective/Open."

College. University.  We think we know what these terms mean, and yet, any discussion of colleges in the US invariably leads to someone saying, "It depends on what you mean by college."

For instance, there are about 6,900 post-secondary institutions in the US, but only 2,654 offer a bachelor's degree; they enroll 10.5 million of the 17.6 million undergraduates.

Of all the institutions in the US, only 293 enroll at least 15,000 undergraduates, but this small fraction of colleges enrolls almost 40% of the undergraduates.  Conversely, there are over 4,300 options that enroll 1,000 students or fewer, but collectively they enroll only about one million students.  Our nation's public community colleges enroll over 6 million students on just over 1,000 campuses.

This visualization should give you plenty of options to see the shape of the higher education industry in the US: Filter and select to your heart's content, and as always, reset using the controls at the very bottom.

What surprised you?

Monday, June 20, 2016

Public University State Tuition

Note: The visualizations are not optimized for mobile.  A desktop is recommended for best viewing.

From the annual College Board Trends in College Pricing comes some interesting data, which I've combined into one database for visualization, focusing on public university tuition for residents and non-residents.  This looks complex, but it's pretty simple.

The opening view shows six charts: 2015 tuition for residents; for non-residents; and the premium a non-resident pays (in sticker price) across the top.  On bottom are three scatters: Resident tuition as a function of state funding per FTE student; five-year, inflation adjusted tuition for residents and not residents; and funding per $1000 of personal income and resident tuition.  Of these, I think the middle is the most compelling: Note the states that have raised tuition faster for residents than for non-residents.

The chart starts with US Averages in red, against the states as gray.  Use the control in the middle to highlight a single state on all six views.  As always, hover over any point for details, and use the reset arrow at lower left if you get stuck.

Using the tabs across the top, you can navigate to the map view.  Choose any value at top right to display on the map.  That value is displayed on the state, and the tiles (representing the states) are color-coded.  Red is high; blue is low.  Click on any tile on the map, and a summary of that state appears at the bottom.

Would your state legislator find this valuable? If so, I'd encourage you to forward to her or him. Otherwise, leave a comment at the bottom, letting me know what you see.

Wednesday, June 8, 2016

Public Institutions and Low-income students

Note: Visualizations are not mobile friendly.  I recommend a laptop or desktop for viewing this site.

Someone asked me today about what I thought higher education's biggest challenge was, and I said college costs without thinking.  And a few hours later, I still think that, with a twist: College costs for low-income students, especially at public institutions who presumably have a primary mission of educating students of all income levels in their state.

To be sure, costs are too high at private institutions, and many of the trends you'll see here are carried over and amplified in the private sector; but private colleges and universities may exist for different reasons, and that can be hard to capture in a visualization like this.

There are two views here, using the tabs across the top.  The first is a scattergram, arraying almost all 660 US, four-year public colleges and universities that admit freshmen (a few are missing data).  The x-axis shows in-state tuition in 2013, and the y-axis shows net price for freshman students who come from families with incomes of $30,000 or less, and who are paying the in-state tuition, most of whom are presumably in-state residents.  The color shows the percentage of students enrolled who receive a federal Pell grant, a program for very-low income students.

Reference lines show the unweighted, institutional averages, which allows the creation of quadrants, roughly:

  • The upper right, or high tuition, high net cost
  • The lower right, or high tuition, low net cost
  • The lower left, or low tuition and low net cost
  • The upper left, or low tuition, high net cost 

Color here is important: Red dots are those colleges with lower percentages of Pell students; blue dots show higher values, although I've capped the color range at 40%, about the national average, if you include all types of institutions.  It's important because it shows how many students these institutions enroll, not just how well they do at reducing price (if they do.)  In other words, it's a bit easier to do a lot to reduce cost for students if you don't do it for very many; it's harder on your budget if you enroll more.

You can limit the view to states, regions, Land Grant status, or by using the filters to show only institutions with certain admit rates or Pell percentages.  As always, take a look at California.  Well done, California.

The second view shows in-state tuition over time, accompanied by net price for three groups of students who receive aid.  Students from:

  • Families with income of less than $30,000 (gold)
  • Families with income of $30,000 to $48,000 (orange)
  • Families with income of over $110,000 (the highest band reported in IPEDS).  This is in blue.

The bottom chart on the second tab simply turns these numbers into an Net Cost: Tuition ratio.  A value of 1.5, for instance, means that the net price is 1.5 times tuition.  Note the definition of net price:  

Net cost shows all costs associated with cost of attendance, minus grant aid.  For example, a university may have a tuition of $5,000, but a cost of attendance of $17,000 to include housing, meals, transportation, and personal expenses.  If a student receives $10,000 in grant aid, that student's net price is $7,000, which is greater than tuition alone.

As always, hover for details, and use the reset button at lower left if you get stuck. 

What do you see here?  What else would you like to see?