Data Journalism Class Exercise (Or, Teaching Critical Thinking)

Here’s a great exercise for journalism professors who are introducing their students to data-driven journalism. It provides a good opportunity to show them that they have to get over the common perception that data is unbiased — clean and clear. It gives instructors an opportunity to talk about the need to “interview” the data.

The assignment is deceptively simple: Have the students download the Census Bureau’s list of rural and urban counties and calculate the population density for the counties in your state.

That’s it. Tell them no more. Depending on where they get stuck, slowly reveal to them the clues they need to complete the project. What you may not be surprised to find is that too many college undergrads seem to be accustomed to following step-by-step instructions and too few know how to break down a problem into smaller, sequential pieces. This is the kind of critical thinking skills that they need to be good journalists. Or, as I like to say, think journalistically regardless of their eventual profession.

Helping Them Get Unstuck

Force your students to get a quick start. Don’t let them sit and stare at their computer screens for even a second. Agitate them in whatever way you need to make them feel like an asteroid is about to smash the earth to smithereens. They can’t solve the whole problem all at once, so what are the pieces of the problem hidden inside this big problem?

  • Where can you find the Census list of rural and urban counties?

The answer — of course — is Google. So, there’s an opportunity to teach efficient search strategies.

Students will click around the Census site a bit trying to find what they want. Ask how skimmed and how many read every word on each page. A good opportunity to talk about the way people use information online.

You can help students find the data they need. And from there you can show them basic file-management and Excel techniques. Where does the file download on their computer? What’s the difference between a .csv and a .xlsx file?

With the data open in Excel, they’ll need to sort to filter out just their state. But now what? Ask the students what they think each of the columns represent. What does it mean that something has a POP_UA of 10791 and a STATE of 37?

Once they figure that out, they may note that the data includes some pre-calculated population density. But it’s not the information you asked them to find, so they’ll have to calculate population density — a commonly-needed, very simple journalism math equation.

This gives you a chance to explain that numbers are only meaningful in relation to other numbers. And how to do basic calculations in Excel.

The students will do the math correctly, but they won’t get answers that make any sense. A chance for you to talk with them about how data still has to pass the sniff test. Why doesn’t the data make sense? They can find the answer back on the Census website.

Once they’ve made the correct calculations (how many meters are in a mile anyway?), you can talk with them about how you still need to find the story in the data. Even though their calculations have added value to the data — essentially refining raw ore — mere presentation is of marginal value.

You can top off the conversation by coming back to language, and that journalistic aspiration for precision and objectivity. What does “rural” mean anyway? What does the dictionary say? Is it an abstract concept or something you can measure? How (many different ways) does the Census measure it? How is it different than the USDA’s definition? Which is better? Why?

This is a project that could take several weeks as a module in a college class, or as a MOOC or quick conference or newsroom workshop. Its strength is its scope and flexibility. Just like a good journalist.