Friday, March 28, 2014

When Good Data Go Bad

Not that long ago, we talked a bit about data quality---attributes of your data that describe its "truth." A recent post over at Education by the Numbers caught my eye, because it speaks to the possible affects of poor data quality. The post pulls out the following quote (emphasis added by me):

The federal report found that barely half of Georgia’s high schools offered geometry; just 66 percent offered Algebra I.

Those data are just plain wrong, said Matt Cardoza, a spokesman for the Department of Education. The state requires Algebra I, geometry and Algebra II for graduation, so all high schools have to offer the content — but they typically integrate the material into courses titled Math 1, 2, 3 and 4, Cardoza said. He surmised that some districts checked “no” on the survey because their course titles didn’t match the federal labels, even if the content did.

“It’s the name issue,” Cardoza said. “I think schools just didn’t know what to say.”

Ah, data quality has reared its ugly head...and now everyone is freaking out about the perceived inequity of math offerings.

So, here's the deal. Suppose you're a working at a high school. You offer an algebra class...but you might not call it Algebra I. You might call it just plain Algebra. Or Algebraic Thinking. Heck, you might even have Advanced Algebra or Honors Algebra or 9th Grade Algebra. At the school level, this distinction doesn't really matter. The school has a master schedule, assigns highly qualified teachers to whatever sections it has that they identify as math. When a new student shows up and needs a math credit, everything in the student information system enables the placement.

But that isn't the end of the story. There's another layer of data that few---maybe just the registrar or district data manager---will ever see. There's a whole taxonomy of course codes determined by the National Center for Education Statistics. These course codes are collected by the state and are part of the district student information system. But because the district doesn't use them for anything---remember, they have their own labels---not many pay attention to what fills those fields.

Here is one example (click to embiggen):

These are the math classes for Bellevue High School for the 2012 - 13 school year. (data source here). Columns 4 - 6 include state course labels---the invisible ones---and 7 and 8 are designated for the district. So, let's dig into the last row ("Mathematics-Other") and see what the district is lumping in there.

Notice that in the second column from the right---District Course Title---we have things like Alg I Seminar, Gmtry Seminar, G-Alg 1 Seminar. We can't see the syllabi for these classes, but it's likely that algebra and geometry concepts are being taught. Kids are getting math credits and are being scheduled into math classes, but a data pull at a state or federal level will never see these.

It gets worse. Start digging through "miscellaneous" categories, and you start to see things like this:

The state course code on the left says English Language and Literature-Other...and the district has assigned biology, chemistry, physics, nanotechnology, and more to this category. Even assuming these are courses for English language learners, special education students, or other population, it's still science content---it doesn't belong in English. At this level, data quality is a real mess.

But what to do? After all, it doesn't make a difference to the district. They have their own codes and credit systems. It does make a difference to anyone outside of that system. It's public data. Anyone can use it for any reason---from bureaucrats trying to make decisions about allocations to think-tanks sounding the alarm about equity.

All of this mumbo jumbo doesn't mean that schools with larger minority populations aren't being underserved. Considering the other ways we shortchange these students, it doesn't seem unlikely that access to a rigorous curriculum would be on the list. I suspect, however, that noise in the data quality is hiding the true signal.

I tell teachers all the time that paying attention to data quality is the simplest way to have a direct effect on policy. You might not think that attendance you took in first period matters...but it does. As it rolls up, districts will make decisions about how they make resources...states will consider policy. How many absences before a student should be considered "at-risk"? What strategies work best to improve attendance rates? What should be the legal consequences for students or parents when kids don't attend school? All the little pieces of data matter. If you want better policy, we need better data quality.

Monday, March 24, 2014

Lookin' for Data Love

I recently attended (and presented) at the 69th annual ASCD conference. If you're unfamiliar with the acronym, ASCD is one of the largest and oldest professional organizations for educators. The letters used to stand for the Association for Supervision and Curriculum Development, but that definition was scuttled a few years ago when the organization outgrew its original boundaries. The acronym was kept for branding purposes.

You might remember that I went hunting last year for data tools in the exhibit hall. I did so again, along with attending a couple of presentations on how data is being used in schools. So, here's the wrap-up.

I looked at three different tools in the exhibit hall. None were quite to the level of a student information system, but all integrated assessment and performance data. Two are not worthy of further discussion (one, in fact, admitted that they do no testing/accommodations for accessibility).

A third, Schoolzilla, didn't totally blow me away; however, they are using Tableau to build their reports. The reports follow the Shneiderman mantra of "Overview first...then zoom and filter...details on demand." To be fair, I don't expect any product to knock my socks off in an exhibit hall setting and where I spend <5 minutes at a booth. However, Schoolzilla may be worth a more in-depth look, if your district is on the hunt for that sort of thing.

I also spent a chunk of time at another venue chatting with a rep from SchoolCity. Their product is recently undergoing a complete redesign, but I got a behind the scenes peek at things. I suspect that this product may well be worth a second look in the coming months.

Again, my socks remained firmly on my legs. Okay, so they were imaginary socks---the conference was in Los Angeles and it was too warm to wear such things---but let's just go with the metaphor here. The presentations I attended were focused on sharing how a particular school or program was using data. The common thread among all these was that no one starts with a question---and I find this worrisome.

Tweet by Science_Goddess: Data use that doesn't start with a question worries me.

While it's good practice for student assessment to guide the next steps in teacher instruction, it is impossible to use every single piece of data derived in the classroom. We have to focus---we need to be picky about where we dig. I know it isn't as simple as that. The hardest part of any analysis is that very first step: Asking a good question---the one most worth asking.

Switching it up for final session for today. "Grab a shovel: Data digs to drive student achievement." (Don't shovel and drive, kids.)

I was pleased to hear one presenter talk about how too many teachers see the purpose of data as sorting and selecting. I became worried, however, when she mentioned how "all the data is spewed across the wall in the War Room." My colleague often says that we have to get beyond admiring the problem. Data can be used in strategic ways, to be sure, but that means respecting what we collect and being thoughtful about how we move forward with it. There is something troublesome, for me, in any terminology that involves spewing and war.

As for me, my presentation went well enough---I even ran short, although no one complained about getting away early. :)  The next morning, this happened (well, after the earthquake):

Tweet by @science_goddess: Just got asked "Aren't you the data woman?" Why, yes...yes, I am."

This data woman hopes to see you at the 70th annual ASCD conference in Houston next year!

Friday, March 14, 2014

Data Sharing for Good

On Sunday, I'm presenting a session at the ASCD annual conference on using data in the classroom. Along the way, we'll take a tour of chart selection for a data set, some best practices for data viz, and tools for moving beyond "admiring the data." Materials for the session, along with other sundry data viz resources are here.

I've tried to pull from a variety of sources...a few favourite quotes sprinkled in, like this one:

We'll play a little game with the When Excel Gives You Lemons data. You can play, too, by clicking here to choose a bachelor from the three below. (Turn your speakers up for the full effect.)

We'll explore a few different kinds of stories...and the problem with pie charts.

A detour to Sesame Street will help us think about pre-attentive attributes.

So, if you're in Los Angeles this weekend, stop by and learn with us. If you have ideas and resources to share, leave them in the comments.

Monday, January 13, 2014

KO Your Data with DQ

The decisions you make using data are only as good as the data themselves. When you look at a score in your gradebook, review a report from the state about test scores, or access a national database with discipline data, you're depending on quality data.

There are lots of things that go into building data quality (DQ). Some aspects are more technical---does the student's name use alphabetic characters (no numbers or symbols)? Others are more utilitarian---is the student's name in correct column? Is it spelled correctly? Is "John Smith" the same as "Johnathan Smith" or "Johnny Smith" or "J. Smith"? And some attributes are more about what the data represents---is the score for John Smith really his (and not some other student's)? Is the score in the box from the most recent math assessment (and not the reading assessment)?

Data quality isn't just one person's responsibility. It's everyone's job, whether you're collecting, entering, or using data. It's also a far bigger issue than I can cover in one post. If you're looking for additional resources, check out the Data Quality Campaign. I also advise you to develop your own bullshit detector, because a lot of what going into using data sets is questioning whether or not they make sense. For example, if one school is reporting 60 suspensions a year...and another school in the same district that is the same size is reporting only might want to ask a few questions.

For today, however, let's talk about a few things you can do in Excel to help with the technical components of data quality.

Excel has several options for cell formatting, including all sorts of customizations you can design. Sometimes, it even tries to be helpful by deciding what format you meant. Perhaps you, too, have been the victim of adding data like 9 - 12 only to have Excel automagically change it to 12-Sep. And when you change the format to specify Text, it further mangles your original entry to 41894 so you have to start all over again. Things like this show you how easy it is for data quality errors to creep into your workbook.

Other times, you may inherit or download a workbook where data quality has not been a priority. At this point, looking for data quality issues becomes a lot like data cleaning.

Now is the time to whip out a few of those functions that you always wondered why they were there: ISTEXT, ISNUMBER, ISNONTEXT, ISLOGICAL, ISBLANK. I know, these seem like "duh" formulas---why do you need a function to tell you whether or not there is text in the cell? Because like our sample above and our list of options, looks can be deceiving.

Here are a couple of examples of how they work. For a full list of IS functions, definitions, and examples, visit this page. Using a formula like ISTEXT or ISNUMBER returns a value of TRUE (Yes, the information in the cell is text/number.) or FALSE (No, the data in the cell isn't text/number.) They are written like this (evaluating whether "Excel Rocks" is text):

And look like this after you hit Enter:

Here's how things look for a FALSE statement using ISNUMBER:

And after:

But let's face it, you're not likely to create an an entirely new table of true/false values and then identify things from there. It's more likely that you would combine these with conditional formatting to highlight the types of data you have.

If you have a data table like this one (data source here):

You can combine these simple statements with some conditional formatting to get a quick visual for your data and easily spot problems.

I've applied two rules to the section of the table with the data. The first identifies numbers and the second text. I chose some fill for the cells, but you could just as easily pick something else.

Remember, Excel can be conflicted about things. For example, the cells with the dashes are formatted as numbers, but Excel is still reading them as text. This is why it's always important to do a quick check for data quality issues either before you submit or before you use a data set.

Depending upon your role, you have some decisions to make. We have cells in our data table that are blue---meaning that they contain text. If these were data I was going to upload to a database or otherwise share, then I need to address these DQ issues. If I'm the database administrator, I might wonder why these cells aren't calculated like the others and need to investigate some code. If these are data that I am going to use for making decisions about where to direct resources for next year, then I'm going to need to find additional information.

Keep in mind that Excel functions a bit like a spell checker in these situations. It can tell you if the right sort of data is in the right spot---but it can't tell you if the names and numbers are the correct ones for those cells. You'll need additional validations for that...or a really good eye and careful work.

Data quality starts with you---from how you enter attendance, label your gradebook, or even type a student's name. But you can use Excel to help you along the way. Building a culture in your school that includes DQ at all stages can lead to better decisions and outcomes.

Tuesday, January 7, 2014

What's the Story, Morning Glory?

The tagline for this blog is Use data to tell your story. In fact, that is also the title of a few presentations I've been doing in 2013 (and am scheduled for in 2014). Whether you use Excel, R stats, Google Apps, or other tools, finding the story in a data set is a complex set of circumstances. Sometimes, you feel like you are trying to divine meaning from a set of numeric entrails. It's often difficult to know where to begin...or, when you're finally at the end, to feel 100% confident that you have honored the best story.

It's important that you be the storyteller. Yes, I know, many of you work in districts where some sort of software will spit out reports for you. Maybe it's your gradebook or benchmark testing. Those reports can be very convenient, but I would like to remind you that they are a developer's idea of what you need---they may not provide the level of insight (or quality of visualization) that supports your work. I know they save time, but with so much riding on the decisions made from these, shouldn't they be all that they can be? Don't settle for this junk. Own your story.

Exhibit OMG: Typical DIBELS Report

But where to start? Whether it's the scores from your gradebook, the annual data dump from state testing, monthly fiscal updates from the business office, or a download from a benchmark testing site, we're all faced with the same question: Now what?

Here's the deal. Working with data is messy. I'm not going to give you any hard and fast rules here ("When you have x data, always do y!"), because frankly, they don't exist. However, we can look at some workflow ideas that will build your capacity to interact with your data. I promise that you won't have to reinvent the wheel everytime. If you know you're going to get DIBELS data every two weeks...and report card scores every six...take the time to figure out your strategies once and then apply them consistently.

So, let's start with the same data set as the previous two posts. It has both categorical (ethnicity; staff and student) and longitudinal (2004 - 2013) data. You may work with a lot of data that has similar features. For example, a gradebook has both categories and time-bound information. So does a fiscal spreadsheet a school business manager might use. You can download the workbook for this post here.

Clean the Data
This idea needs a separate post, but for now, let's just acknowledge that you often get stuck with "dirty data" that you will have to spank into shape. Are the numbers formatted as numbers? Do you have missing data points...and is that okay? Is it organized and labeled, ready for use in charts? What about data quality---are the data valid and accurate? (This last idea is also another post unto itself, which we will save for later.)

Explore the Data
When you have a new-to-you data set, and it's all tidied up and ready for church, make a few different charts. Remember, the reason we make charts and graphs, instead of only using tables, is because we are reducing the cognitive demand while increasing the amount of understanding. By this, I mean that it would be very difficult, at best, to keep all the numbers in the table in your head while simultaneously interpreting the results. A picture really is worth 1000 numbers. 

Which ones, you ask? Do you start with a line chart? A column/bar chart? A scatter plot? You may have to try more than one. I always recommend the Chart Chooser as a starting point.

It's not the only tool out there to help you think through what you might want to look at. You might also like the Visualization Options over at Many Eyes. Or the Classification of Chart Types over at the Excel Charts blog. If you really want a deep dive, bigger ideas than just the charts themselves, read Resonate by Nancy Duarte (it's free!). Find something that helps you think through what you want to show (e.g., part-to-whole, relationships...).

Get in there and make a couple of pictures. At this stage, it's okay if they're ugly---they won't be your final products. You just need to see what story to pull out.

We did this in previous posts. We looked at a column/bar chart using these data and a line chart. They're totes ugly. However, they do show us a couple of things. First of all, we can see that only one population of students has had consistent growth over time (Hispanic), another has had a significant decrease (White), and the rest have remained about the same. For staff, there is not as big of a story, but it echoes what we see with the student data. The number of Hispanic teachers is increasing (3.5 to 4.9%), the number of White staff is decreasing (90.4 to 87.6%), while the rest remain steady.

So, now we have a better idea. We need something that highlights the two changes (or at least one of them for further discussion).

Tell the Tale, Nightingale
A lot of people think making a basic chart is the end. Even if you see what you need to see, be sure to clean them up. Beyond that, extend your thinking about the best way to show the data.

What about a "win-loss" chart for these data?

This is just a basic column/bar chart in Excel, except I've had it plot the overall change, instead of year-by-year. There may be times when we care about the data for in-between years---when we're trying to spot patterns in the fluctuations among groups. But perhaps it's better to just cut to the chase using a chart like this one. The drawback to a chart like this is that it doesn't give you a perspective on the proportions of each population as a part of the whole. Sure, white kids aren't as numerous as before, but what we can't see is that they're still close to 60% of the total.

Or, what do we notice if we plot the data as small multiples?

This type of chart is a combo of several charts, allowing you to make comparisons among groups. (Jon Schwabish has a great tutorial for creating small multiples using Excel.) The big thing here is to keep the axes among all the charts the same and to line up your charts so it is easy to compare across groups. I could have also done this version as a column/bar chart or even separated students from staff. These charts could even be reduced further to sparkline form, and we'd still get the idea. I always find small multiples to be a very busy way to present data, but they do allow you to spot patterns---and, especially, common patterns---much more easily. For example, in every case except White, there is a greater percentage of students than staff for a particular ethnicity.

Which one is the "right" one? No hard and fast answers here. Like any story you tell, you need to consider your audience and purpose. Sometimes, the audience is just you, the teacher, trying to decide where you need to go with your instruction tomorrow. Other times, you are trying to build a case for a school board to allot money for a capital project. Or influence policy. But the students we serve deserve the best stories we can share. Take the time to develop the best one you can.

Bonus Round
Did you know about HelpMeViz? It's a place where you can both give and receive support for data visualization. Go have a look, offer your ideas, or seek feedback on a project of your own.

You might also enjoy accidental aRt, a tumblr devoted to visualizations that turned out a little more interesting than anticipated.

Wednesday, January 1, 2014

When Excel Gives You Lemons, Part II: Line Charts

In our last post, we looked at how to take a default column (bar) chart in Excel. Most of the same "clicks" we used can also be applied to a line chart. Keep in mind that these are not hard and fast rules, just some guidelines to help you along.
  • Add a title with description and label axes, using simple text/numbers.
  • Reduce the data-to-ink ratio by reducing the intensity (e.g lightening up) axis and grid lines, axis labels, and font colors; and, removing tick marks.
  • Use color and data labels to emphasize main points.
If we start with the same data set on the ethnicity of Washington K-12 staff and students from 2004 - 2013, we get this as a default line chart:

Who needs eyebleach? Maybe a unicorn chaser?
I don't like a line chart for these data any more than I liked the basic column/bar graph. There's nothing wrong with using a line chart, per se. It shows all the data points---both categorical and over time. But because of the way our brain automatically assigns differences to groups of items vs. individual, it really draws the eye to the "white" data. And if that was the story we needed to tell, that would be okay...but it isn't. We'll get back to this in another post.

So, let's talk about this line chart for a moment. The default colors here are really a problem. If we're going to display staff and students on the same chart (and there are good reasons not to), then even a basic clean up of the line colors can help the audience make some comparisons between staff and students of a particular ethnic group. I also recommend labeling the lines, instead of using a legend. If we do these things (the bulleted list above and these additions), here's what we get:

Ahhh...that's better.

That's not too shabby. While I still don't like a line chart for these data, I do think that it does a good job of showing the relative proportion of the ethnicities and gives a bit more story. The populations of Asian,  Black, and American Indian students have held fairly steady over the last 10 year and kept their rank (by number) over that time. (I.e., Asians have always had the third largest population of students...Native American the smallest.)

Do we need the marker points and all of the data labels? Personally, I don't think it's necessary with this particular chart. Why not? Because we're not trying to make direct comparisons between the groups. It's not the big idea. That being said, if you wanted to add marker points, I wouldn't fuss. If you really need all the data labels and your chart is looking too busy, consider adding a table with the data for those who need the deets.


Again, with less than five minutes of work on a basic chart, you can have a big impact with your message. We still have some digging to do. What is the best way to present these data? What is the "So what?" of student populations rising/declining---do we also need to consider professional development needs of teachers, test scores, geospatial data (is the effect the same throughout the state), and so forth? But all of these start somewhere, usually with a data dump like the one we've used here. The more you do to communicate and present your data effectively, the more clearly the next steps will appear.

Bonus Round
You can download my anatomy of the redesign here. Again, it is not intended as a "Thou shalt...," but is offered to provide a reference of where to click and ideas to consider as you build charts of your own.