Click on our Doing Economics projects today and you will find that they have been revised and expanded, as part of our continuing commitment to helping students, whether they are studying economics or not, develop the data-handling skills that will be important both to their job prospects and for society.
Lord O’Donnell, former Cabinet Secretary, and now chair of Frontier Economics, tells us that he supports our ambition. “The key to making good decisions is to have good data, analysed well,” he says about about our Doing Economics projects, “Developing data-handling skills is vital for jobs in both the public and private sectors.”
So what are we doing to help develop those skills?
The spreadsheet version of our original projects was designed for Excel users, and of course we are still supporting Excel. But some teachers told us they would like to take advantage of the superior collaboration potential in Google Sheets, so that teams of students, or students and teachers, could work simultaneously on one project. So now we are supporting Google Sheets too.
If you haven’t used Google’s apps, you might be surprised to find how seamlessly they support collaborative editing: this might be an asset if you want to work on the projects in tutorials and workshops, or if your students are not all in the same location.
We’ve changed the way we discuss statistical inference, p-values and confidence intervals. The debate over the arbitrary 5% cutoff that is considered “significant” has been rumbling on for many years, but it was given fresh impetus in Nature last year with an editorial titled, “It’s time to talk about ditching statistical significance”. This was in response to an article by Valentin Amrhein, Sander Greenland, and Blake McShane (backed by more than 800 signatories) that criticises the pervasive reliance on using p-values in this way (see “Scientists rise up against statistical significance”).
Section 2.3, particularly the “Find out more” box, is an example of how we discuss p-values now. In short: instead of asking students, “Is the difference in means statistically significant?”, we encourage students to ask themselves, “What do the p-values tell us about the difference in means?”
If the commitment not to put little stars next to empirical results fills you with dread, watch this explanation by Margaret Stevens, of CORE’s steering group, about why we made the change. She gave this presentation during the Doing Economics session of CORE’s 2019 teaching and learning workshop. The discussion of p-values and significance starts at 6:38, but if you are interested in using Doing Economics in your course, the whole session is worth a watch.
Excel looks a bit different in Windows and Mac versions (picture, right), and so we’ve improved and expanded some of our walkthrough videos (specifically, this one, this one, this one, and this one) to avoid baffling our Mac users.
Students can complete the same project using spreadsheet software, or use them to help learn how to programme using R. Ralf Becker, a lecturer in economics at the School of Social Sciences, University of Manchester, is the driving force behind the R versions of the projects, which have been revised and improved. He recommends teaching R to undergraduates who want to learn the data analysis skills that Lord O’Donnell advocates, rather than more economics-specific statistical software packages.
“We now teach R to all the undergraduate students who take our courses and it has been a real success,” he says, “It used to be that statistical software training was a real chore, and convincing students to do it was so hard, but that has changed. Even if they use some other data-handling software later, the skills they are learning are generic programming skills, and this has made it so much easier for students to engage.”
As always with CORE, Doing Economics is free and open access, and (like learning R) the best way to get to know it is to dive in.