Often the news -- even local or hyperlocal news -- is in the numbers. But as recent election coverage showed, data illiteracy is a problem for many journalists. Here are some ways to sharpen you data analysis skills in order to create stronger, more compelling community news coverage.
First of all: What is data literacy? According to the Data Journalism Handbook (an excellent book available for free online), data literacy is: "The ability to consume for knowledge, produce coherently and think critically about data. It includes statistical literacy but also understanding how to work with large data sets, how they were produced, how to connect various data sets and how to interpret them.
Data literacy breaks down to a set of basic skills, including:
- Learning key statistical terms, like the difference between mean and median; or why a standard deviation or margin of error might matter.
- Knowing what questions to ask about data or a statistic to gauge its potential relevance, quality or reliability.
- Performing basic statistical calculations -- nothing fancy, just enough to do a quick reality-check whether you're understanding the story that a dataset might be telling.
- Putting data in context, such as considering the local unemployment rate in the context of Census data for your community, or local vs. state/national crime statistics.
Reading or taking an online or in-person course are probably the best ways to start boning up your data literacy skills. In addition to the Data Journalism Handbook I mentioned, the Data Literacy blog by social psychologist Barton Poulson offers many succinct and fun lessons, examples and resources.
To get up to speed on basic statistical concepts and issues, start with Statistics for Journalists, a brief resource by Robert Niles of Online Journalism Review. Also see Statistical terms used in research studies; a primer for journalists, by Leighton W. Klein. For more statistics resources see this guide from the Knight Center for Journalism in the Americas.
Also, even though it's nearly 20 years old, out of print, and not available as an e-book, John Allen Paulos' 1995 book A Mathematician Reads the Newspaper is an entertaining and compelling collection of essays that also walks you through some of the basic calculations. Especially intriguing for community news publishers is section 2, where Paulos explores how to define "local" by relevance to your community, or groups within your community -- something that could influence which types of data you seek or data comparisons you make.
If looking at numbers makes your eyes glaze over, try using data visualization tools to help you ask questions of, and see patterns in, data.
If you want to move beyond data literacy into doing data journalism, you'll want to learn some additional skills, such as:
- Finding relevant datasets. Who's gathering data about (or relevant to) your community? What form is this data in, and how can you get it?
- Searching data. Asking useful questions and getting useful answers by manipulating databases or spreadsheets.
- Cleaning data. Most datasets are "dirty" in some way -- inconsistent, incomplete, not well organized for your purposes, or containing a lot of extra stuff you don't need. The trick is to decide which datasets can be cleaned up, and how to do that without destroying or skewing their value.
- Visualizing data. There are many tools for turning numbers into pictures that tell stories -- charts, infographics, interactive data visualizations, and more. (See Placeblogger founder Lisa William's recent KDMC webinar, Diving into Data)
For finding and starting to play with datasets, Lisa Williams recommends: "Go to Data.gov and find a small dataset that interests you -- something that you could load into a spreadsheet. It doesn't necessarily need to be about your community, just something interesting. If it has less than 10 columns or says 'summary,' it's promising. Export and download it, and then upload it into Many Eyes."
Many Eyes is a free set of online data visualizations tools from IBM. You can create several different views of your data there -- from word clouds and bar charts to maps, scatter plots, and more. Experiment to get a feel for which types of visualizations tend to highlight patterns in which kinds of data. There are also many datasets already uploaded to Many Eyes by its users that you can explore with visualization tools. (Remember: any data you upload to Many Eyes is publically visible, so don't upload confidential or personal information there.)
Remember that data which is relevant to your community need not be necessarily directly about your community. Comparing local statistics to statewide or national trends, or to statistics from communities elsewhere that share certain characteristics, can be illuminating. But in order to judge the relevance, usefulness, and value of data -- even well enough to confidently cite statistics in news stories, let alone create interactive data visualizations -- basic data literacy is required.
Fortunately, you can get started today. What were the five statistics your community news outlet cited most recently? What were their sources? What context can you place those numbers in, and do they seem to add up? What might you see if you turn those statistics upside down, to reveal complementary questions? (For instance if there's a 8% drop in local unemployment, does that really more local people found jobs?) Can you access the data behind those statistics? Tracking down and exploring at least one dataset for a statistic you previously cited might yield an interesting followup story.
The Community News Leadership 3.0 blog is made possible by a grant to USC Annenberg from the John S. and James L. Knight Foundation.