Wednesday, December 17, 2014

Another 1000 Words and Ten Charts on First-generation, Low-income, and Minority Students

I have always enjoyed writing, and I consider this and my other blog like a hobby.  Usually, I spend no more than 45 minutes on any post, as I don't make my living by writing, and my blogs are not "monetized." But once in a while, an opportunity presents itself to write for a wider audience, and that's when I see what it takes to make a living putting words to paper. That happened this week.

You may have seen my opinion piece in the Chronicle of Higher Education. If not, you can read it first, read it last, or not at all; I think both this and that stand alone, despite their relationship.  In the end, we ended up with about 40% of my first draft, which is what happens when you write for a print publication. And of course, a print publication makes interactive charts, well, difficult.

I think there is more to say on the topic, because the similarities in recruitment challenges for first-generation, low-income, and minority students tend to look a lot alike, and the more data you look at, the clearer this all becomes. So I took what I had pulled together to research and write the CHE piece, formatted it for this blog, and put it here with the understanding I'd not publish until the Chronicle piece was online.

Frankly, this topic is a little bit personal for me.  My parents, born on farms in Iowa in 1916 and 1917, were among those who never attended high school, let alone college; of their four children, I'm the only one with a college degree. So I was a quintessential first-generation college student, and as I wrote on my other blog, my father made $17,000 in his best year, (or about $48,000 today's dollars) which would qualify us as low-income by most standards. In fact, I've always pointed to those factors as powerful influences in my choice of a profession.

I think the story is an interesting one, and I hope you'll stick with it to the end.

Point 1: The challenge of increasing attainment is not a new one, and America has made considerable progress in the past on this front. Consider this mind-blowing statistic: In 1940, about 60% of adults, like my parents, didn't make it past 8th grade. A lot of that generation who lived through the Depression and World War II sent their kids to college in large numbers in the 1960's and 1970's, increasing attainment rates dramatically.

The first chart here is very detailed and interactive, and the second is very simple, but they are based on and show the same data.

With all the charts, hover over a data point for details.






Point 2: Is education inherited?  That is, does it get passed on from parents to students like silverware and pocket watches?  Some data from the Brookings Institution suggests so: If your parents are educated, you're likely to be, and if they're not, the same is probably true of you. Anyone who's done admissions understands there is a cultural component and a context sensitivity that are important when recruiting first-generation students. Students whose parents have never been to college don't navigate the process as well as those who do.

To read this chart, look at each column, representing the educational attainment of fathers, from low on the left, to high on the right.  Each column adds to 100 or so (rounding errors) and the boxes represent where 100 children of fathers with this level of education end up in the educational attainment spectrum. The children of fathers with lower attainment end up near the bottom in greater numbers; the children of well educated fathers end up near the top (blue boxes are higher numbers). Orange boxes (low numbers) show up when we see how many children move in either direction; gray boxes are numbers in the middle.

Feel free to argue nature, nurture, or systematic issues here.  We like to think education is based on meritocracy; in fact, it may be an "inheritocracy."



Point 3: If education is inherited, this presents a problem that affects students of color, because African-American and Hispanic students are less likely to live in homes where a parent has a bachelor's degree.  Thus, they cannot "inherit" educational attainment. So ethnicity = attainment, to a large degree.




Point 4: And not surprisingly, college attendance rates already vary dramatically by both income:




And by ethnicity:


So income = ethnicity and ethnicity = attainment and attainment leads to attainment. (And I use the term "equals" pretty liberally here, of course.  It's obvious that nothing is destiny.)

Point 5: There is also the issue of affordability: Students of color tend to come from families with lower income levels. And I doubt we need a chart to demonstrate the relationship between parental education and family income.  But it may be that believing you can't afford a college education, accomplishment notwithstanding, may be the biggest barrier to educational attainment we have.



On one of the criteria that is weighted heavily in admissions, standardized test scores, students of color and low-income students tend to score lower.  If colleges and universities are serious about enrolling more first-generation students, low-income students, or students of color, they need to take a serious look at the weight of tests in the admissions process, and the extent to which they let the public judge them on freshman test scores.  While I'm generally agnostic about the US News and World Report rankings and the pursuit of them, this is the one area where I believe criticism is justified.

The 50 boxes for each ethnicity (five test score bands by ten income levels) are colored to show the percent of total by ethnicity, and those values add up to 100. Blue values are higher, and orange values are lower.  So you notice that the bluest box for African-American and Hispanic students is in the lower left corner (low income, low scores); for Asian and Caucasian, it's near the upper right (high income, high scores).  Hover over any box for details; this is from ACT EIS Data for 2010 and is created by compiling spreadsheets extracted from the software.


Of course, no one believes that being Hispanic or poor is the direct cause of lower attainment on standardized tests. Many believe the lower scores are proxy values for opportunity, and there are many who believe that standardized tests simply reflect the accumulation of social capital throughout a child's lifetime.  And much of the social capital necessary to be admitted to college comes with wealth and parental attainment.

Point 6: And finally, I offer these two charts.  First, the relationship between freshman test scores, ethnicity and Pell at American Colleges and Universities. You can look at private and/or public by using the filter, but regardless, you can see the relationships played out: In general, the higher your freshman class SAT, the lower the percentage of freshmen on Pell, and the less diverse you are. Thus, when we ask universities to be "excellent" and we define "excellence" by input variables like SAT or ACT scores and selectivity, this is what we're left with: Colleges who want to do the right thing have to act counter to their own interests.


And this: The relationship between institutional wealth and economic profile of the freshman class. You can click here to read the blog post when I originally published it, if you'd like.




So, I hope the points and the patterns in them are obvious: When we talk about increasing college attendance among first-generation, low-income, and minority students we're really talking about the same thing.  And all those factors pull, unintentionally, against the very things many colleges and universities strive to achieve.  It will take some very brave institutions at the top of the food chain to break out of the pack and move us collectively in the right direction.

I think it's a vicious circle of our own doing. What do you think?





Monday, December 15, 2014

Looking at Student Loan Default Rates

Student Loan defaults make a lot of news, but there is not a lot of understanding about what a default actually is, and there is not good, easily accessible data on default rates, nor a lot of good contextual analysis.  But this may help a little.

First, the source of the data is here.  You should read it, especially  the part about small numbers of students entering payment, or small percentages of students taking loans at a college skewing default rates.  You should also know that the definition of a default is being at least 270 days behind on a payment.

This is not the easiest data to work with.  For one thing, the file layout descriptions don't match the file; Financial Aid uses a different ID than IPEDS, and the crosswalk tables that might help you figure out the IPEDS ID (to get a richer view of context) use a different format than this table does. In addition the "Region" doesn't roll up the states in any way I've seen before, and the "Program Type" also puts colleges in categories that don't always make sense.  For most four-year institutions, try "Traditional" first in the selector box.

But here it is.

If you want to eliminate the small schools that skew things, you can use the "Borrowers Entering Repayment 2009--11" filter.  You can just type the ranges in the boxes and hit enter, or use the sliders.  You can also limit to states or region, in any combination.

A reminder that outputs are sometimes actually inputs.  If you enroll high ability, wealthy students, and are very selective in admissions, your default rates are going to be lower than other institutions that take more chances on students who come from low-income or less-prepared backgrounds.  It would be great if there were a way to recognize the institutions with lower default rates who took more risks.

What jumps out at you?



Thursday, November 20, 2014

What is the Pell Grant Worth?

The Pell Grant got its start as the Basic Education Opportunity Grant, or BEOG sometime in the 1970's; it was later named for Rhode Island Senator Claiborne Pell.  The idea was simple: To provide a basic level of financial support for students from low income families who aspired to go to college.

It's almost certainly had a lot to do with increased levels of educational attainment in America, but rapid tuition increases, coupled with lower increases for the Pell, means the gap between Pell and tuition has gotten bigger over time.

The College Board has complied a lot of good data on this and other financial aid trends in its report, updated annually, on its Higher Education Trends site, where you can download the data. Unfortunately, the data looks like this when you get it.  Maybe you can extract the insight; I can't.



So I pulled it into Tableau and spent an hour or so with it to see what I could find.  It's below.  The College Board has calculated enrollment-weighted average tuition by type (4-year public and 4-year private, not-for-profit) which makes comparisons easier, and has adjusted everything (including the maximum Pell Grant) for inflation.  You can see on the three views what's happened to tuition, fees, and Pell; how much they've changed on a percentage basis; and the purchasing power of Pell over time.

Occasionally (OK, frequently) I've criticized highly selective institutions for enrolling very low percentages of Pell Grant students in their freshman class.  If you wanted to argue that they don't make much business sense, you might have a point.  But you'd also be right in pointing out that the diminished purchasing power of Pell is due in large part to rapid increases in tuition. So there's plenty of blame to go around.

What else do you see?




Saturday, November 15, 2014

Four Ways to Look at College Tuition in the States

College tuition is a hot topic these days, in case you've not noticed.  But often, the stories focus on a national perspective: Tuition is up a gazillion percent in ten years, for instance, or average tuition increases last year were 4.9%.  Sometimes, big states like California get attention because there are so many students affected by tuition increases (and because 9% of all college students in the US are in California Community Colleges.)

So I took data from the College Board, and looked at it four different ways, to show how the story changes depending on where you live.  Each of these four views requires you to choose a type of institution (public four-year, public two-year, or private not-for-profit), and to choose whether you want to look at inflation adjusted tuition or nominal (non adjusted).  I recommend inflation-adjusted (unless you're interested in nostalgia) because it gives you a better sense of how much tuition has increased. One view (at the bottom) also allows you to compare over any period of time, by changing the beginning or ending years.

Before beginning, just a caution: Different states have different numbers of private institutions, and varying levels of selectivity among them.  I'm not sure the average tuition and fees at private universities in any state is especially meaningful, but I've included it here anyway.  The data was free, so ignore it if you agree with me on this point.

And while the story of this post is tuition, there is a sub-context as well: The things that interest me about data visualization.  The story you want to tell drives the way you choose to display your data. The corollary, of course, is that the display you choose limits the story you can tell. So choose wisely.

Here's the first.  On this you must also choose a focus state, by using the control on the right. The state you select then changes color to show where it ranks among the states.



Next is a view of all the states, with the rank of tuition represented by color. Red is high, blue is low. It gives you a good perspective, I think, on the geographic effect on tuition: Northeast and West Coasts are high, while the middle of the country is low.  What you see in rank, though, you miss in spread; you can't tell how much separates #32 and #33, for instance, like you can on the first view. So the bar chart below should help you there.



Next is a heat map; it neither arrays the data on a map to allow the geography, nor tells the spread, but it does show you the entire data set over time, relative to rank: Has any state gone from blue to red, or vice versa (meaning its rank had changed?) Quite the contrary, of course. Most states stay pretty stable in their position relative to each other.




And finally, a good old line chart.  See the trends for one state, both with regard to actual dollar amounts for the three different types of institutions, and the percent change over time. The spread between the three different types is most interesting, I think.  Compare California to Vermont to Florida to Texas, for instance, to see how those states' philosophies vary.  Note that you can change the time window by de-selecting any years to leave just the ones you want.



Tuesday, November 4, 2014

More on Endowment Resources and Low-income students

I've just finished writing an article for the Chronicle of Higher Education (which is why I haven't posted in a while) talking about our first-generation, low-income student challenge.  This chart didn't make the final cut because the article has a lot of charts, and I tried to keep them simple.  But I do think it's instructive.

Each dot represents an institution; you can hover over the dot to see details.  They're colored by freshman selectivity (red dots mean institutions with a freshman admission rate of under 15%, using 2012 IPEDS data, and green is admit rates between 50% and 75%, for instance.)  They are arrayed on two scales: Endowment assets per FTE student enrollment (high to low, from top to bottom) and the Full-pay/Pell Spread. To calculate the Full-pay/Pell Spread, you simply subtract the percentage of undergraduates with Pell Grants from the percentage of all students who receive no institutional aid (also know as full-pay students.)  Institutions on the right side of the zero have more full-pay, institutions on the left have more Pell students.

If you want to eliminate some of the noise, use the filters to limit the view. In fact, limiting by mean SAT scores, or by state allows you to see some interesting patterns in the data.  Sometimes, what you see is really something else disguised in a more friendly name.

What do you see?



Monday, October 13, 2014

College and University Enrollment in 2021

The National Center for Education Statistics (NCES) provides a lot of snapshot reports of national education data, but it also produces projections of educational statistics out ten years that can prove useful.  Unfortunately, they're always a couple years behind the curve, so this is from the 2012 report (the 2013 report is out, but only available in pdf format.)

It tells an interesting story of college enrollment that can be helpful as we look into the future.  By 2021, overall enrollment will increase with the US population; it will still be mostly Caucasian students, although that majority will have shrunk; and Hispanics will have overtaken African-Americans as the second largest ethnic group.

If your institution isn't thinking about the future, and especially if your administration is still dominated by people who went to college in the 1960's and 1970's, you should show them this.

Is this a case of everything changing?  Or everything staying the same?  Let me know what you think.



Friday, October 10, 2014

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.



Friday, October 3, 2014

Where do International Students Enroll in the US?

Here's some more interesting information from the IIE Open Doors Project, but this is not about where US students study overseas, but where students from overseas enroll in the US.  This is pretty simple, actually.

Each dot on the map represents a college or university (hover over for details).  The size of the dot represents the International Student population in 2012, and the color represents the percentage of enrollment at that college or university that is international (that is, on a J-1 or F-1 Visa, presumably; permanent residents are not considered international students.)  The bar charts below show every IHE with both the number of international students (right column) and percentage of all enrollment that is international (on the left.)

You can use the filters to narrow down the IHEs displayed, by choosing the number of international students, the percent international, or the percent of all enrollment that is international.  And you can sort the bar charts by hovering over the small icon that appears when you hover over the top of the bar chart.  Clicks cycle through displays.  The circular arrow at the bottom resets it all.

Note: The IIE data does not break out enrollment by graduate/undergraduate, so I cannot calculate the percentage of undergrads or graduate students who are international.  The only thing I can show is the total international population as a percentage of all students enrolled.



Wednesday, October 1, 2014

Students Studying Abroad from American Universities

Lots of US college students study abroad, and the IIE Open Doors Project has great data about where they go; if you're a member, they graciously make even more detailed data about what colleges and universities they attend.

So I took that data and rolled it (from 2012) into my IPEDS data set (from 2013) to see what jumped out at me.  On this visualization, you can sort the top chart several ways to see who comes to the top of the list: Alphabetically, by the number of students studying abroad, by the percent of all enrollment or the percent of undergraduate enrollment.  Just hover over the top of that column until the small icon pops up, and click on it.  It will sort ascending, descending, and alphabetically with subsequent clicks.

Note, I've taken a few institutions out because they are mostly graduate and thus have undergrad/study abroad percentages off the charts.

The bottom chart is more interesting, I think, for the pattern it shows: For public institutions, a higher percentage of freshman with no institutional aid (full pay) translates into a lower percentage of students studying abroad.  For private institutions, it's just the opposite.  Does that suggest anything to you?

Note: An update.  I was asked how this can show an institution sending 15% of its students overseas while that institution claims 50% of its students do so.  The IIE data is for a single year, and it does include graduate students, so it's not perfect.  Thus, it's possible that 15% study overseas in one year, but over time, half the students at an institution do.


Changes in Degrees Awarded Over Time


As we continue the push to enroll and graduate more students from colleges, it's interesting to look back at trends in degrees awarded, at the Bachelor's, Master's, and Doctoral levels over time.  So, I downloaded data from the Digest of Education Statistics and came up with this.

At the very top level, we see that the number of degrees awarded has increased dramatically since 1970, although of course so has the population: It's up about 51% since 1970, while degrees awarded increased 138%, while bachelor's degrees increased just 113%.  You can't say after looking at those statistics, however, whether we've been successful or not; along with the increase in population comes shifts in age distributions, as well as ethnicity and wealth, all of which affect the likelihood of going to college.

Still, the interesting stuff comes as you scroll across the gray boxes on the top, and drill down by academic discipline: Decreases in English and Education, and increases in business and health professions at the undergraduate level; increases in some narrowly focused programs at the graduate level.

Play around. Filter and click.  What do you find interesting?


Monday, August 25, 2014

Where students enroll

It's the end of summer, so this is a quick and easy visualization, showing enrollment in 2013 by institution on a map.  The data is provisional IPEDS data, so it's subject to some corrections, but the overall patterns won't change. It's probably obvious: Most colleges and universities are in populated areas, and so most college enrollment is too. But a few clicks can point out some interesting differences between and among the regions in the US. 

Take a look at the first map, showing the US population. Every county in the country is colored from green (low) to red (high) populations. It should come as no surprise that most of the US Population lives on or in the east coast, the midwest, and the west coast.

Now click on the tab at the top to to see where college students enroll, and you'll see a similar pattern: The orange and purple circles represent four-year public and private, not-for-profit institutions; the size of the circle is the relative enrollment.

You can use the control at the top right to show undergraduate, graduate, or total enrollments, and then you can filter the views down.  Start with New England, for instance, and take note of the color and relative sizes of the enrollment.  Then go somewhere else: The west coast, or the south, and see how the patterns change. Then try the same for graduate, and zoom around the country.

This raises an interesting question: How much of this correlation is due to putting colleges where people are, and how much is due to colleges attracting people to the city, state, or region where they live?


Monday, August 4, 2014

Make Room at the Top

Higher education is obsessed with prestige and institutions are always clamoring to find new ways to make it into the exclusive clubs in which they see their peers: Most selective, most applications,highest test scores, or  biggest capital campaigns, for instance.

Has the top gotten bigger? I looked at IPEDS data from 2004 and 2013, and focused just on those whose numbers say are in the upper echelons of higher education, notwithstanding the limitations of IPEDS data.

Use the tabs across the top to see the Tableau Story Points and see for yourself how the world has changed.The good news might be that if you're a student, there are more "elite" colleges these days; the bad news is that some of them are harder than ever to get into.  And that makes them happy.


Friday, August 1, 2014

2013 IPEDS Admissions Data

My now-annual visualization of IPEDS admissions data is now up.

This is always a popular post, for a lot of reasons: Counselors like to use it as a resource: journalists tell me it helps put things in perspective; and alumni rush to see how their school did last year (admit it, I know you do!)

There are several tabs across the top to show test scores, admit rates, applications received, and other interesting data points. To get the most of it, you must interact, so click a filter to see only a region, or limit the view to schools of a certain size. You won't break anything. If you do, just click the little recycle button near the bottom to reset anything.

IPEDS data is often wrong (more often at small schools with limited or no IR Staff) so take some of this with a grain of salt. And this always involves a lot of typing sans spell-check.

If (when) you see something, let me know. Until then, enjoy.


Monday, July 28, 2014

Where Students in the US were enrolled, 2012

A while ago, I wrote about the wide range of institutions in the US post-secondary education system.  The point I was trying to make was that the prestigious, extraordinarily selective institutions you hear about all the time represent just the top of iceberg when it comes to colleges and universities in the US.

That visualization counted institutions, for the most part.

But another way to look at this is to show where students enroll, and I think this can be equally surprising, and, I hope a bit enlightening.  So take a look at this, again using Tableau Story Boards.  Each gray tab across the top shows the data presented in a different way; keep clicking from left to right to see the interesting tidbits this data reveals to us.

Did you know, for instance, that one out of every eleven undergraduate college students in the US (excluding for-profits) enrolls at a California Community College?  Or that there are no private, not-for-profit colleges in Wyoming? Or that the state with the highest percentage of students enrolled in four-year public universities is...well, you'll have to find that for yourself.

Interact:





Tuesday, July 22, 2014

Changes in Faculty Salaries Over Time

Note: The first version of this had a bug; I forgot to add the "Gender" filter to the final view, so everything was showing up at about 3 times the actual value.  It's fixed now, with a thanks to Tableau Zen Master Allan Walker of Utah State University for catching it. While I was at it, I added a second view to show differences by gender.

The last time I wrote about the salaries of educators, I said I'd never do it again: There were too many people who didn't know the difference between nominal and constant dollars ("No one made $75,000 in 1980!") and those who didn't understand averages ("How could the average in that state be $60,000? I only make $53,000!").

But this is interesting, I think in light of discussions about the rapid increase in tuition over time (in case you've been under a rock recently.)  It shows the changes in average (there's that word again) faculty salaries by rank since 1975, in either nominal or constant (inflation adjusted) dollars.

Compare these changes to this and see what you think:



Tuesday, July 15, 2014

A DataViz Reboot: WICHE Projections of High School Graduates

A while ago, I used Tableau Software to visualize trends in High School Graduates provided by WICHE.  I think it was good, but with Tableau's new Story Points feature (in which you create pages of the story you want to tell) I think it's an even better story.  If you scroll through these points, you can get a sense of how America has become more diverse, and how those changes vary pretty dramatically by region.  That last point, especially, is often lost on people who talk about changing national demographics.

Just like all politics is local, almost all enrollment is too.

So, first, if you want, look at the old version, then take a look at the new visualization below.  What do you think?


Saturday, July 12, 2014

Where do International Students Enroll in the US?

In recent years, Colleges and Universities have turned their focus to International Enrollment as a source of new students. But is that a good idea?  It depends on what type of institution you are, apparently.

This data comes from the Institute of International Education's Open Doors project, and while it's valuable, it still points out the problems with pre-aggregated data.  On the site, you'll find good stuff about students by enrollment level (graduate and undergraduate); you'll find good information about enrollment by institution; and you'll find information like this about enrollment by Carnegie Classification.  But you can only ask one question of each data set.  The result is that this information is intriguing, but not granular enough: For instance, what if I presumed that graduate students would naturally flock to doctoral and research institutions and a) I wanted to test that theory or b)I  just wanted to look at undergraduates to see where they went?

Still. have a look, and especially use the year filters to see percent change over long and short periods.



Wednesday, June 25, 2014

US Post-secondary offerings

If you only read the papers, you'd think US Higher Education consisted of a dozen or so high profile institutions.  But fortunately, there are "more things in heaven and in earth than are dream't of in their philosophy," with all appropriate apologies to Shakespeare.

When I started this blog, it was in response to a new Tableau Software feature I had seen pre-viewed last September, called "Story Points."  In fact, the very title of the blog has a lot to do with that: Believing that data can and should be used to tell narratives that provide people with memorable insight.

This is my first attempt to use Story Points to tell a story; one I hope sticks with people as we think about a pretty amazing selection of post-secondary options for students.  To navigate the story points, just use the grey boxes along the top, and a new chart or dashboard should point the way to insight.


Monday, June 23, 2014

Predicting EFC with One Question

Most everyone who knows anything about our Financial Aid system thinks it needs some improvement.  And almost everyone who actually goes through it, it seems, is astonished by the outcome: They expect me to pay how much for college? And that's just for one year?

For those of you who don't know, all federal financial aid begins with the FAFSA, or Free Application for Federal Student Aid.  It's a form that collects information about income, assets, and family size in an attempt to estimate how much a family should be able to contribute to the cost of higher education.  Should being the operative word.  The figure it calculates--EFC, or Expected Family Contribution--is really a misnomer, sort of like the Peacekeeper Missile.  It's really just an index number designed to estimate federal expenditures on financial aid programs.  Many colleges find it so unreliable that they use another form, such as the College Board's Profile, or their own proprietary form to collect more information.

Many people believe the complexity of these forms causes many low-income students, especially those from immigrant families, to opt out of college.  And a new bill has been introduced in Congress to simply the process, by possibly asking only two questions.

Debate is underway, and you can search it to read about it, but I began to wonder: What if we asked just one question: Parental Adjusted Gross Income.  What is the current relationship between AGI and EFC?

I took about 57,000 records from three years of freshmen applicant to DePaul data, and plotted them (after I made them anonymous and created a fake ID number).  Then I created a trend line to fit the data, and added confidence intervals to the lines.  Note that only about half of the students who apply for admission fill out a FAFSA.

The dots are colored by the number of students in the family in college, which has a affects EFC at any income level.  If  you want a less cluttered view, select a single year.

What do you think?  Could we simplify the process?





Wednesday, June 18, 2014

Bachelor's Degrees by Program and Ethnicity, 2010 and 2011

The previous post, about Doctoral Degrees by Program and Ethnicity, generated a followup question from Jennielle Strother at Seminary of the Southwest about similar data for undergraduate enrollment.  While I couldn't find that exact data, I did find this from the Digest of Education Statistics, showing degrees awarded by race and program, so I spent a half hour to pull it into a visualization.

Some data visualization experts don't like tree maps because it's hard to make precise comparisons of area across distance, but I do like it for this purpose: You can pretty easily see the data in one view with minimal effort, and since precise comparisons aren't vital, you can get a good sense of the lay of the land.

It's also very easy to ask your questions of this chart.  For instance, if you want to see how degrees shook out within a program (like engineering, or English)  you can quickly make those selections and see the results by ethnicity.  If you want to exclude non-resident students, for your analysis, you can. If you want to see what Hispanic students majored in, you can look at all programs but select just"Hispanic" on the ethnicity filter.  Just make your selections in any combination and click "apply" on the filters.  And choose which year you want to look at.