Which statistical package?

My research stretches across clinical psychology, behavioural neuroscience and neuropsychology (and now, with a smidge of epidemiology thrown in). Researchers in these fields use different statistical approaches, and reflecting this, different packages. SPSS is common, although the neuroscience contingent (especially the younger researchers) tend to use R. I’m a long-time, disgruntled SPSS user. Disgruntled because it’s often clunky and slow, it’s a pain to produce attractive graphs, and it’s also expensive. (It’s free through most universities, but if I wanted to use it on my own it would be a fairly costly yearly subscription fee.)

Therefore, I’ve been teaching myself R, which has a rather steep learning curve, but is probably the best long-term option. In the meantime, I’ve also trialled other free packages that are easily available, namely PSPP and JASP. (I’ve also dipped a toe into STATA, which seems like a pretty good – paid – program, but haven’t used it enough to talk about it at length.)

R is definitely the most powerful and flexible, and the graphs it produces are very pretty. It’s open-source, and it has a large international user base. New packages for R are always being developed, and support is usually available (although I find that those who ask questions are similar in skill level to me – i.e. novices – , whereas those providing answers are…not. Which often makes the answers hard to understand.) I use R Studio, which provides a nicer environment through which to use R. That being said, it’s not a point-and-click interface at all, so to use R you have to use syntax/coding. There are lots of free web-based tutorials on using R. One of these, which looks good for new users, is provided by The Analysis Factor.

PSPP is an open-source take on SPSS, which looks and feels very similar, and as such it’s a great choice for someone needing relatively basic stats but not wanting to pay for SPSS. Pros: it will read SPSS dataset and syntax files, and syntax is almost identical, so if you’re familiar with SPSS it’s an easy transition. Even if you just need to get some data from SPSS files into something else, this will work. You can also edit data in it, i.e. create new variables, edit values within your variables, etc. Cons: It doesn’t have the full functionality of SPSS (e.g. GLM does not support continuous variables/covariates), and graphs are *very* basic. From memory it’s also a bit fiddly to install, but good instructions are available and it’s definitely worth it for a free “SPSS lite” program.

JASP is a good little program. Pros: it is very clean and simple to use. It reads both csv and SPSS (*.sav) data files, and it does some surprisingly funky stuff like some Bayesian analyses (not that I know much about Bayesian statistics at present). While it only has a limited number of analyses available, it does these well, and produces nice graphs, very easily. Cons: It is all point-and-click, i.e. you can’t use syntax. You also can’t use it to edit data at all. It lacks some more advanced options available in SPSS and R, such as bootstrapping.

For now, until I get better at R and can switch to using it full-time, I am using bits and pieces from all these packages (…as I procrastinate by writing this blog post instead of working on my second paper…).