No more excuses: R is better than SPSS for psychology undergrads, and students agree

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.


6 thoughts on “No more excuses: R is better than SPSS for psychology undergrads, and students agree

  1. I wouldn’t focus the attention so much on students who don’t have problems with the transistion, but those who do. That said, did you also evaluate RKWard ( Its code preview features can help non-programmers to get a feeling for R coding.

    • We have put a number of resources in place for students who struggle with the transition, including online discussion forums and walkthroughs. We’ll see how it goes and report back. We are happy with RStudio because of its RMarkdown integration, and because students don’t need to install anything: they can connect to an RStudio server through a web browser.

  2. Your experiences echo ours at Queen Mary: we moved to teaching R instead of Minitab to our undergrads about 5 years ago and were pleasantly surprised by the response. The things they seem to appreciate are (in no particular order) being able to plot high quality graphs easily, getting experience in writing code and being taught to use top-end professional software.

    We make sure that we have plenty workshops for them to get practice with a good ratio of staff and demonstrators to students, and this seems to be what makes the difference. The students ho don’t do well are the ones who skip the workshops and then have a nasty surprise when they get their assessed coursework and find out that they can’t even import a .csv file. The grades for the module tend to bimodality for this reason.

    Our Geography and Psychology departments, by contrast, (I’m a biologist) are hanging on to SPSS like some sort of statistical security blanket. This is their choice but it causes problems when we have joint classes because when there are complicated analyses such as doing NMDS on community data I can easily give the biologists an outline script to help them and point them in the right direction, but I can’t do that for the geographers and the psychologists.

    • I am glad to hear that you had a similar experience. Another thing we are seeing (anecdotally) is greater confidence among our students with respect to data analysis. I agree that support is critical! There is massive information about R on the internet, and lots of places to get help, but much of it is hard to for a novice to sort through. We use a Slack discussion forum where students can choose pseudonyms, and it works pretty well.

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