Getting It All Laid Out

One of the MOOCs I'm currently enrolled in is Alberto Cairo's Introduction to Infographics and Data Visualization. So far, it's familiar territory, which is nice. Some things are new to me, but I am not overwhelmed because everything is new to me. It's a six-week course. We're starting the third week and have our first offline assignment.

Imagine that you report to me (your managing editor in a news publication). You wish to make a proposal for a visualization based on these numbers. How would you convince me that your idea is relevant? You will need to show me detailed sketches (made by hand or through a design program) to do that.

By "these numbers," he is referring to these data about the changing number of tenured faculty at U.S. universities. I've downloaded the data, but I'll need to do some more reading and digging before I'm ready to do something with them. I have to figure out the "So what?" before I draw up a presentation of the data.

In the meantime, I thought I'd share a bit about the process I use when building a data display...something I worked on this week.Over the next month, I'll be sharing some data with groups of small districts. The data is not so much a report---they already have some of it in various forms---but something to explore in common. These groups of districts will be trying to identify some common ground as a starting point for some work together.

I started by finding all of the data I could about these districts: fiscal, staff, and student-level. Then I pared down the data sets by thinking about what the audience would want to see. Next, I got out my pencil and some scrap paper. It was time to draw some scenarios. I think that even if my digital tools weren't limited to Excel, I would still begin with an analog model. I like to list the types of things I want to show and then figure out how to arrange them. Mind you, this is only a starting point. I often find myself in the middle of developing something, only to find out that it didn't make sense after all.

Finally, I get knee deep in Excel. For this project, I ended up with three different displays: one that showed overall trends in student performance, another to dig deeper into performance in various subject areas, and a demographics overview for each district.

Here is the first one of the series. I am still struggling with it in terms of whether or not to go with clustered columns instead. Such a display does make it easier to compare data over the years, but since I am including both regional and state level data for three different years, clustered columns get a little busy. The reason why I am leaning toward the version shown is that it is easier to compare patterns between the region and state.

There are things I don't like about this display, so if you have some ideas about fixing it, I'm all ears. For example, I'm not convinced about using different colors for the bars in the top row, but since I'm not going with the clustered columns, I think the color helps make comparisons across the years. I really don't like the labels along the bottom set of charts. I suppose I could shorten them and then create some sort of legend that gives the real version. There are two choices a user can make in the interactive version of this chart. They can pick a subject area (reading, math, writing, science) and grade level (3 - 8)---so not all of the labels are as cumbersome as they are for reading.

The second display shows trends for graduating cohorts---although these students may be many years from walking across the stage. The purpose of this display is to look at performance for the same set of students. For example, how did a group of fifth graders score when they were in fourth and third grades? The current grade levels of students are in parentheses.

It's a little busy. Mind you, you need a big monitor to view the spreadsheet all at once. I've tried to be as consistent as I can with the color scheme, headers, etc. I do think that the small size of the graphs is a bit misleading---some of those gaps are as much as 20 points. Users can hover the cursor over points to see the numbers associated with them.

Finally, here is a sample of the district overview.

It's the only one of the three that doesn't have comparisons, but in this case, it should be okay. It will serve as a reference when teachers from each district talk about their schools. I have some stacked bar charts here to help conserve space, but I've tried to keep the style more or less the same. I know that the more I fuss over the details, the less they will be in the way of others making sense of what is presented.

For me, these sorts of designs are a slow process. The last graphic took me most of a day to derive. Sometimes, graphs don't turn out in the way you think they will. Or they take more space. As hard as I try to be consistent with colors, labeling, and fonts---there is always something I miss. Sometimes, I build something only to find out that I don't need it. And, no doubt, the day after I share these, I will see two or three other ideas I wish I would have incorporated. But I'm feeling pretty good at this point in the build.

Now, about those tenure data...guess I've procrastinated long enough...

Where's Frankie Avalon When You Need Him?

Image credit

MOOC-y school dropout,
No certificate for you.
MOOC-y school dropout,
Can’t do R-stats worth a poo.

Okay, so maybe it's not quite that dire. But my first big programming assignment for my R-stats class is due in 30 minutes...and after many hours over several days, I still can't get even the first part of it to work properly.

Baby don't sweat it (Don't sweat it),
You're not cut out to write a script.
Better forget it (Forget it),
Who wants a program that can’t do shit?

I'm not really dropping out, but I think I will be giving up on my dream of completing the class in good stead. There are only two more weeks, so I will keep up with the lectures and quizzes...maybe poke around in the programming innards some more (even if I can submit the assignment). I will learn what I can and not lose sleep over the rest.

I'm a pretty good problem solver when it comes to messing around with formulas. I understand how to go out and Google for help and find YouTube videos I can follow along with. But those skills aren't serving me well with R-stats. Perhaps another course will help push me along.

I've called it quits, 
to bytes and bits, 
They really made me cry!
Think I’ll be going back to spreadsheets in the sky…

Mind the Gap

I want to share an idea I saw at a conference last month. Presented by Paul Stern of the Vancouver Public Schools, it was one of two very intriguing concepts for working with assessment data. Fair or unfair, schools are the subject of a lot of comparisons---how well they perform against other schools in their area, state, or even nationally and internationally, as well as internal comparisons that look at scores from year to year. We can think of lots of reasons why these "apples to oranges" discussions are cagey---everything from the populations schools draw from, to the curriculum used, to teacher quality, parent involvement, and so forth.

Perhaps the biggest of these---in terms of what school staff discuss or dismiss---is the percent of students eligible for free/reduced lunch (FRL). Often used as a measure of poverty, the greater the percentage in a given school, the greater the population living at or below the poverty line. There are some quarrels with using this. For example, the percentage decreases as grade levels increase---that is, there are far more students in kindergarten who are eligible vs. high school seniors. This may be due to underreporting at upper grade levels (a kid doesn't want to appear different in front of peers, and so the paperwork doesn't get turned in), or simply that as children age and become more independent, it's more likely to find two working parents outside the home (and therefore more income). But, we'll set this aside for today's discussion.

So, here's a chart that will serve as the starting point for us.

The dots on this chart represent every school in the state of Washington for which data were available on performance of 8th graders on the state math test and percent of students eligible for free or reduced price meals. The dark orange trendline tells us about what we'd expect: the greater the percent of students eligible for FRL, the lower the percentage of students meeting the standard (a/k/a "passing the test"). They straight beige line shows the statewide percentage for meeting the standard on the 8th grade math test.

Looking at this might engender some questions about schools that don't fit the overall model. In the lower lefthand corner, we have schools with a low percent of FRL...but poor performance on the test. And in the upper righthand corner, we have a few schools with a large percent of FRL, but are doing better than the statewide performance. What are those schools doing, I wonder?

But let's say that you're in a large district, like Seattle. It's likely there are conversations about students achievement at the middle school as it relates to poverty, but we can dig deeper than that. We might expect a certain level of performance, based on the model shown above. But using the model to supply a context will allow us to remove poverty from the discussion---in other words, what is the gap in performance between the predictive model and the actual score?

Here is the same chart, with Seattle schools highlighted (click to embiggen):

As we can see, some schools, are below the trendline---they didn't score as well as predicted. Others are above the trendline---they performed better than predicted. To help visualize this a little better, let's zoom in on two of the schools.

The arrows point to the predicted performance of McClure and Pathfinder. Based on their percentage of students eligible for free/reduced lunch, we would have expected them to score around the state level (~55%). However, McClure scored 13 points above this...and Pathfinder 6 points below.

We can also build a chart to take a broader look at the various gaps between predicted and actual performance. Using the handy-dandy formula for slope that Excel provides for this trendline (y = -0.362x + 68.088), we can substitute the percent of FRL for x and find the predicted performance based on the trendline (y).

See? Your Algebra teacher knew learning about slope would come in handy someday.

Using one of the stock charts in Excel, we can visualize this to get a better idea of the differences in performance.The schools are organized, left to right, by their predicted performance. The dot at the end of each line represents their actual performance. The length of the lines shows the difference.

This chart helps us see things in a new way. For example, Madrona has the highest percentage of FRL out of these schools, but their gap in terms of expected performance is certainly not as big as Cascade or Orca. Hamilton has the lowest percentage of FRL and the highest actual math scores in the district, but it is not the school that best outperformed expectations. This also allows us to see that schools like Jane Addams and Madison, while still performing below the state average, are outperforming expectations (if only by a small margin). We don't celebrate our successes nearly enough in education. Maybe that's because we don't look for them like this.

Again, the idea here is to remove poverty levels as the focus for explaining the differences between schools. Doing so allows us to look for deeper answers about curriculum and instruction. This is not to say that socioeconomic status has no impact---just that dismissing low performance because of is not the whole story.

I've used public data available here to model these charts, but you could substitute other indicators. Education is certainly not all about the test---and schools shouldn't be judged on a single measure. But I do think that this could be a powerful starting point for schools and districts.

Learning All the Time

Over the next two months, I am participating in three different Massive Online Open Courses (a/k/a "MOOCs"). It's good to remind myself now and then that the edge of my rut isn't the horizon. It's time for me to make my brain hurt again.

I've taken online courses before, but never with so many classmates---and not in such a low-stakes way. Although I want to make the best effort I can, not paying for the courses (or having them show up on a transcript) gives me a bit of an "out," if I get overwhelmed.

Here is what I've signed up for:

Computing for Data Analysis

This course started on Wednesday and runs until January 30 (and has ~40K people enrolled). Taught by Roger Peng from Johns Hopkins, the description states that "this course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods." Yikes. I haven't had an ounce of formal programming coursework since high school---you know, back when BASIC and the TRS-III were king. But I hear about R a lot and am curious about how to use it. It's time for me to get on board.

So far, so good. I've completed watching the lectures for week one and have taken a ton of notes. I'm almost finished with the first programming assignment/quiz---just one question left that has me stumped. I know how to solve it with Excel. In fact, the entire assignment would be much easier (for me) in Excel. But I am trying not to "cheat" by doing it in Excel first and then checking my answers in R. I need to know how to program...and the only way to do that is to get my hands dirty with R.

The most important thing I've learned so far is that syntax can be an unforgiving master to serve. Excel will give you some leeway between upper and lower case, for example. But R is exacting for every piece. 

But, hey, I've survived the first week in good stead...and that's 25% of the course. I feel more confident (even if it's a false sense) than I did when I signed up for this. Maybe I really can do this.

Introduction to Infographics and Data Visualization

Some of you may remember posts on other data-minded blogs this fall about this course led by Alberto Cairo. This will be the second offering, starting on Saturday, January 12 and wrapping up on February 23. This time, it's bigger (6K students) and has a few more tools available.

From the syllabus: "This course is an introduction to the basics of the visual representation of data. In this class you will learn how to design successful charts and maps, and how to arrange them to compose cohesive storytelling pieces. We will also discuss ethical issues when designing graphics, and how the principles of Graphic Design and of Interaction Design apply to the visualization of information. The course will have a theoretical component, as we will cover the main rules of the discipline, and also a practical one, as you will learn how to use Adobe Illustrator or Tableau to design basic infographics and mock ups for interactive visualizations."

I'm totally psyched about giving this one a try. I'm excited about learning the basics of Illustrator. I've played around a bit with Tableau before, but this will give me a reason to go back and dive deeper.

Data Analysis

Because the overlap in the first two courses is apparently not enough for me, this course also starts this month (January 22) and then runs for 8 weeks. So, there will only be one week where I will have to juggle all three...and some time when I just have this one to manage (assuming I don't sign up for anything else). This one is a complement to the R programming class I've already started, and is taught by Jeff Leek, who is also from Johns Hopkins.

This course is billed as "an applied statistics course focusing on data analysis. The course will begin with an overview of how to organize, perform, and write-up data analyses. Then we will cover some of the most popular and widely used statistical methods like linear regression, principal components analysis, cross-validation, and p-values. Instead of focusing on mathematical details, the lectures will be designed to help you apply these techniques to real data using the R statistical programming language, interpret the results, and diagnose potential problems in your analysis. You will also have the opportunity to critique and assist your fellow classmates with their data analyses."

I like stats, but it has been awhile since I have flexed those muscles. Not as long as it has been for programming, fortunately. One of the reasons I am interested in this class is the chance to work with bigger data sets, while applying my nascent R skills and reawakening my statistical knowledge. It's a good application of things. 

I'm hoping that I won't be a MOOC dropout. I also know that I've signed up for a heavy dose of learning at a time when I have a ton of travel for work and several projects due. But Opportunity knocked and I've chosen to invite her in to stay for a bit. If she starts making a pest of herself, I'm giving myself the option of evicting her. These courses will be offered again. I can catch her on another round.

Are you taking any (or all) of these courses, too? I have a couple of study buddies lined up for the first two courses, but the more the merrier! What else are you learning this year?

Spotty Past

By now you've probably seen the Census Dotmap that everyone is talking about. It is "a map of every person counted by the 2010 US Census. The map has 308,450,225 dots - one for each person." When you look at it holistically, it's kinda cool, but you might not feel like there are any particular insights.

Let's see, the east side of the US is more heavily populated than the west. People love to live along coastlines. You can pick out metropolitan areas and assign them a name with ease. In some ways, it's not very different than some other maps we've seen...like the one of the US at night.

Source

But I like maps. I think there are stories in them. And I'd like to tell you one based on this particular point.

Click to embiggen, if you don't believe there's a town there.

I know, it's too small to see at this scale, but at the end of that arrow is the town where I grew up. So, here it is up close:

You are here.

Remember, each of those dots is a person---about 6000 of them. And when I look at this, I not only "see" the neighborhoods where my friends once lived, but also something of the topography. Can you tell where the main road (and train tracks) go through town? Can you tell where the university is, with its abundance of students? Would it surprise you to learn that there's a mountain at the southeast edge of town (where the dots line up, but go no further)? Even without the street labels, I can make a pretty good guess of where my mother's house is, because there is an empty space on the map for the elementary school---just a block away from the house.

Does this help?

What about this? You can definitely see the mountains better.

The interesting thing to me that the Census Dot map does is that while it doesn't hold surprises at a large-scale, the more I poke around in different towns, the more small-scale questions I have. I see things with this map, in terms of use of space and concentrations of population, that I can't see with the other maps.

Useful or not for schools? I think it's another tool in the arsenal. What do those distributions of population tell you about the needs for access to public services---and is that happening? What about arts and culture? School buses are getting pretty sophisticated these days with GIS data, but I still wonder if there is something to learn from a look at population. What would this map tell us about the community we serve?

Go play and tell me what you divine from the dots: past, present, and future.