Starting this academic year (2016/7), all of our stats teaching in Psychology at the University of Glasgow will use R and RStudio instead of Excel and SPSS.

When we proposed the transition, some staff worried that the scripting nature of R would be too challenging for incoming students, many of whom would be starting the program with little or no programming experience. Staff at other universities who have also been leading similar transitions to R have also told me that they have faced similar pessimism from teaching staff.

I did not share these doubts, because for six years, I have been giving students the choice between R and SPSS in my level 3 course, and found that those who chose to use R and RStudio were mostly able to get up and going on their own, even without very much guidance from me, beyond sending them links to online tutorials. To be sure, students found it challenging at first, but these were students who had just gone through two years of the program working in Excel and SPSS, and had become accustomed to the point-and-click nature of these programs. The truth is that you really aren’t exposed to the quirks of R until you start using it for data wrangling–which generally isn’t part of the psychology stats curriculum anyway (but should be!) If all you are doing is plugging in pre-formatted, pre-cleaned, canned datasets, cranking out a t-test or an ANOVA and maybe also a bar graph, the software you use does not make a huge difference. But if that is all you are teaching, your students will be ill-prepared when they first encounter their own messy datasets.

This kind of teaching should be a relic of the past, and this is one of the many reasons we decided to move to R: we thought that R’s more interactive, transparent, and reproducible approach to data analysis would be better for learning, and we also wanted to incorporate more data science skills into our curriculum.

We recently piloted some of our new R materials on a group of undergraduates who already had some exposure to SPSS in previous years, which allowed them to compare the two platforms. Bear in mind that these students had no previous experience with R before the piloting day, and had not yet been subject to my annual brainwashing lecture on the benefits of R at the start of year three.

One of the questions we asked at the end of the session was: “Did you understand how to use the software?” 12/13 students said “yes”, with the remaining student saying “yes and no; with some practice, it should be OK.”

The students’ comments are very illuminating, and I will let them speak for themselves. They have not been edited or cherry picked.

I hope they encourage other departments to make the transition to R.

R generally is fascinating, and I can absolutely see the benefits of it. I am sure it will make students more critically aware about what they’re doing instead of just following SPSS drop-down menus.

R is very easy to use. Typing in code helps with understanding.

Already find R much better than SPSS because I feel much more in control and enjoy having a clear oversight over what I’ve been doing, while SPSS just felt like I was clicking random buttons and looking for numbers to write down.

I find it more engaging than SPSS.

You are able to see exactly what is happening to the stats, you can change the graphs around and if you add a sort of interactive feature, e.g., change the error bars, colour the graph, etc., you can start to see how the code works.

You can edit/manipulate the data much easier than with SPSS. It forced you to engage with it.

Different things are on the screen at the same time so you get a complete overview of your stats. It would help if most relevant results were written out in bold; volume of text can be a bit overwhelming, but I guess that’s how the software is.

If you know what to look for, it can really help with solidifying mechanics behind stats.

I found the experience very exciting and going through it with the teaching assistant was extremely helpful. I personally prefer R to SPSS and can’t wait to learn it and be able to run it. R looks more timesaving and less confusing than SPSS.

Better than pointing and clicking. Coding is much more flexible. But the results of the tests look a bit more confusing than with SPSS.