Category Archives: Resources

FRED’s economic resources praised in today’s New York Times!

Economics reporter Neil Irwin wrote about his technology-related workflow in today’s New York Times. In, “Why ‘Fred’ Is the Best Friend of Economics Writers,” he says:

Economics is a topic full of data. What tools do you use to parse that data? And what sites or apps do you use to keep on top of the latest economic trends?

Every economics writer’s best friend is named Fred. It stands for Federal Reserve Economic Data, and it’s maintained by the Fed bank in St. Louis. It allows you to use a single interface to pull, at last count, 509,000 different data series from 87 different sources of economic and financial data.

A big part of the advantage is simply that once you’re familiar with the interface, which is intuitive, you don’t have to relearn the data retrieval tool for each statistical agency every time. So, for example, I write about the European economy only now and again, so I have to relearn how to use the Eurostat database every time if the data isn’t in Fred. That’s not for the faint of heart.

I generally use Microsoft Excel for data analysis, which is powerful enough to do most of the stuff I know how to do on my own. That’s to say, if a project requires a bigger data set or more complex statistical techniques than Excel can handle, I probably will need help from a colleague with more advanced programming skills anyway.

Or for a quick calculation of, say, percentage change I use a Texas Instruments scientific calculator I keep on my desk…

Congratulations, FRED team!

We were thrilled to have Katrina Stierholz from FRED and Charissa Jefferson of Cal State Northridge introduce FRED at our recent 4T Data Literacy conference. Check out the archived session here.

Using samples in citizen science

Interested in embarking on a citizen science project? One way to learn about the world around you is to take a sample. In fact, this spring the radio and podcast program, Science Friday, encouraged listeners to take samples, which sparked a variety of ideas from listeners.

So how do you go about getting a sample? As Charles Wheelan writes in Naked Statistics, it’s like soup! In all seriousness, best practice is to take a representative sample.  Wheelan explains that:

[t]he key idea is that a properly drawn sample will look like the population from which it is drawn. In terms of intuition, you can envision sampling a pot of soup with a single spoonful. If you’ve stirred the soup adequately, a single spoonful can tell you how the whole pot tastes.

This soup analogy is informative. If a sample is not representative (or the soup is not well-stirred), we cannot make generalizations. Wheelan explains:

[s]ize matters, and bigger is better…it should be intuitive that a larger sample will help smooth away any freak variation. (A bowl of soup will be an even better test than a spoonful.) One crucial caveat is that a bigger sample will not make up for errors in its composition, or “bias.” A bad sample is a bad sample.

From a sample, we may learn something about a population, but we must take care not to overgeneralize. For more on samples and other statistical concepts, Naked Statistics is a useful primer and one of the books that our team has enjoyed.


Source: Wheelan, Charles. Naked Statistics: Stripping the Dread from the Data. New York: W.W. Norton, 2014.

Image: “Pot Steaming Hot Cooking Kitchen Stove Cooker” by Republica, on Pixabay. CC0 Public Domain.

Reading Recommendation: Diary of a Citizen Scientist

One way for you and your students to get your feet wet with data is citizen science. Citizen science endeavors involve collecting data, which make such projects great activities for applying data literacy skills. In fact, citizen science is one of our themes for the second year of this project, starting in the fall!

For ideas to embark on a citizen science project, check out the book, Diary of a Citizen Scientist: Chasing Tiger Beetles and Other New Ways of Engaging the World by Sharman Apt Russell. Russell writes about her project to study the Western red-bellied tiger beetle by the Gila River (pictured above) in southwestern New Mexico. This book is mix of a diary, environmental messages, and how-to guide for being a citizen scientist. Her work will inspire you to dive into a citizen science project. Not only will you learn about Russell’s research but also about other citizen science initiatives, like Galaxy Zoo and Project FeederWatch.

Russell chronicles her successes and challenges, as well as reflects on her motivation for doing citizen science, in the book:

We all want to be part of something larger. We want to be part of a family, a community, a cause. We want to be part of something meaningful. Studies show that long-term happiness depends on this engagement. I personally want to advance conservation policy. I want to do real science. I want to learn more science.

It’s inspiring! This book is in the style of nature writing with both personal reflections and scientific information. Russell weaves stories and tips in with descriptions of her experiences. Reading her account makes a citizen science project seem manageable and provides a great introduction to citizen science.


Source: Russell, Sharman Apt. Diary of a Citizen Scientist: Chasing Tiger Beetles and Other New Ways of Engaging the World. Corvallis, OR: Oregon State University Press, 2014.

Image: Middle Fork of the Gila River, SW New Mexico” by Joe Burgess, on Wikipedia. Public Domain.

Need data? Try

For data about a wide variety of topics, from education to environment, is a great source. This portal for data gathered by the U.S. government offers downloadable files that you and your students can analyze. It’s a good place to get your feet wet working with spreadsheets and data to spot patterns, form arguments, and create visualizations!

You can find examples (under the “Data” tab) to use with your students, and students can become familiar with finding and manipulating data by exploring this website and selecting data sets. Also, demonstrates government transparency and open access to data.

Tip: Look for CSV or .xlxs files to easily download and view in spreadsheet software, like Excel and Google Sheets.


Image: Screenshot of homepage.

Reading Recommendation: Data and Goliath

Where are your data stored, and who has control of your data?

The answer to this question is not always straightforward. We don’t always know whose eyes are on our data. For example, cell phone data reside on servers of private companies. A lot of information can be gleaned from data, from your location to your relationships.

Bruce Schneier writes about surveillance via data in Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. For anyone curious about what data that companies and the government keep and monitor, it is a fascinating read.

One of Schneier’s points is about security and privacy, which pertain to data. Access to data, like cell phone logs, can reduce privacy but support security. He writes:   

[o]ften the debate is characterized as “security versus privacy.” This simplistic view requires us to make some kind of fundamental trade-off between the two: in order to become secure, we must sacrifice our privacy and subject ourselves to surveillance. And if we want some level of privacy, we must recognize that we must sacrifice some security in order to get it.

However, this contrast between security and privacy might not be necessary. Schneier goes on to point out that:

[i]t’s a false trade-off. First, some security measures require people to give up privacy, but others don’t impinge on privacy at all: door locks, tall fences, guards, reinforced cockpit doors on airplanes. When we have no privacy, we feel exposed and vulnerable; we feel less secure. Similarly, if our personal spaces and records are not secure, we have less privacy. The Fourth Amendment of the US Constitution talks about ‘the right of the people to be secure in the persons, houses, papers, and effects’… . Its authors recognized that privacy is fundamental to the security of the individual.

More generally, our goal shouldn’t be to find an acceptable trade-off between security and privacy, because we can and should maintain both together.

Schneier’s book is illuminating for considering personal data management (one of the themes for the upcoming second year of our project in 2016-2017!) in light of data use by commercial companies and government. Schneier takes a philosophical approach to discussing data, security, and privacy. He concludes with useful tips for protecting your data. Read Data and Goliath for some great food for thought!

Source: Schneier, Bruce. Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. New York: W.W. Norton & Company, 2015.

Image: “People Lens White Eye Large” by, on Pexels. CC0 Public Domain. 

Adventures with Correlation and Causation

One of the first things that I learned for this project was that correlation does not imply causation. While it is easy to be critical of misrepresentations of causation, it is much trickier to apply the concept myself! This week, I was composing a research proposal and struggling to design my experiment so that it tests causation. My first iterations would have only revealed correlations. After working with a research professor to redesign my proposed experiment, I added a qualitative test to determine the effect of the independent variable on the dependent variable. This change would hopefully show causation if it existed. My experience taught me what a slippery concept causation is!

To improve my understanding, I revisited one of the books that our whole team read to grow in our data literacy. Naked Statistics by Charles Wheelan covers basic statistics with real-world examples. Wheelan offers a clear explanation of the difference between correlation and causation:

…a positive or negative association between two variables does not necessarily mean that a change in one of the variables is causing a change in the other. For example, I alluded earlier to a likely positive correlation between a student’s SAT scores and the number of televisions that his family owns. This does not mean that overeager parents can boost their children’s test scores by buying an extra five televisions for the house. Nor does it likely mean that watching lots of television is good for academic achievement.

The most logical explanation for such a correlation would be that highly educated parents can afford a lot of televisions and tend to have children who test better than average. Both the televisions and the test scores are likely caused by a third variable, which is parental education. I can’t prove the correlation between TVs in the home and SAT scores. (The College Board does not provide such data.) However, I can prove that students in wealthy families have higher mean SAT scores than students in less wealthy families. (p. 63)

This illuminating passage helped me grasp the distinction between correlation and causation. Televisions do not cause higher test scores but are correlated with them. Digging deeper reveals other variables — parental education and family wealth — that do affect test scores.

From learning how to apply these concepts and going back to a resource, I now have a much deeper understanding of correlation and causation!

Source: Wheelan, Charles. 2014. Naked Statistics: Stripping the Dread from the Data. New York: W.W. Norton.

Image: “Family watching television 1958” by Evert F. Baumgardner on Wikimedia Commons. Public Domain.

Why care about data literacy? Check out these slides

Our team member Jennifer Colby, a teacher librarian at Huron High School in Ann Arbor, put together an informative presentation called “What is Data Literacy: Getting our students from data to knowledge.” In it, she expresses the importance of data literacy in classroom learning and gives four salient reasons, quoted from her slides:

  1. To develop literacy skills
  2. To develop standardized test taking skills
  3. To address state and national standards
  4. To develop informed citizens

Her slides highlight examples of how to approach data, statistics, and visualizations. Data literacy applies to all content areas. Take a look at the infographics on Romeo and Juliet — they give plot insights through their visual representations of events.

In the big picture, data literacy helps students with “understanding,” “extracting,” and “presenting” data. With so much data in school and everyday life, these competencies are key.

Keep her points in mind to incorporate data literacy into your instruction.