I use SPSS for statistical analysis, but I don’t like it. Every time I do, I feel like the victim in some kind of emotionally abusive relationship. The interface is deeply horrid, the outputs are butt-ugly, and it runs like a three-legged overweight sloth with a heavy suitcase. It’s an absolute bloated dog of an application, and IBM clearly don’t give a crap about it, other than making some cosmetic updates every now and again. Plus the licensing system is bat-shit insane, and very expensive.
So, why do I keep using it? Because a) It’s what I learned as an undergraduate/PhD student and I know it backwards, and b) there are few viable alternatives. Yes, I know I should learn R, but I actually don’t use ‘normal’ stats that often (I spend most of my analysis time in brain-imaging packages these days) and every time I learn how to do something in R, I try doing it again a month later, have forgotten it, and have to learn it all over again. At some point I hope to become an R master, but for occasional use, I find the learning curve to be too steep. I would also hesitate to try and use R to teach students; I find it generally pretty user-hostile.
So, for ages now, I’ve been looking for a good, user-friendly, open-source alternative to SPSS. One that isn’t a bloated monster, but has enough features to enable basic analyses. I was quite hopeful about PSPP for a while (free software that tries to replicate SPSS as closely as possible). However it lacks some relatively basic ANOVA features, and since one of the things I dislike about SPSS is the interface, trying to replicate it seems like a bit of a mistake. SOFA statistics was a contender too, and it does have a beautiful interface and produce very nice-looking results, but it only does one-way ANOVAS, so… fail.
So, I gave up and crawled miserably back to SPSS. However, fresh hope now burns within my chest, as the other day I came across JASP (which the developers insist, definitely does not stand for ‘Just Another Statistics Program’). The aim of JASP is to be ‘a low fat alternative to SPSS, a delicious alternative to R.’ Nice. It seems to cover all the analysis essentials (t-test, ANOVA, regression, correlation) plus also has some fancier Bayesian alternatives and a basic Structural Equation Modelling option. The interface is great, and the results tables update in real-time as you change the options in your analysis! Very nice. This demo video gives a good overview of the features and workflow:
It’s clearly very much a work-in-progress. One issue is that it doesn’t have any in-built tools for data manipulation. It will read .csv text files, but they basically have to be in a totally ready-to-analyse format, which means general data-cleaning/munging procedures have to be done in Excel/Matlab/R/whatever. Another major downside is that there appears to be no facility for saving or scripting analysis pipelines. Hopefully though, development will continue and other features will gradually appear… I’ll be keeping a close eye on it!
Researchers typically use a lot of different pieces of software in the course of their work; it’s part of what makes the job so varied. Separate packages might be used for creating experimental stimuli, programming an experiment, logging data, statistical analysis, and preparing work for publication or conferences. Until fairly recently there was little option but to use commercial software in at least some of these roles. For example, SPSS is the de facto analysis tool in many departments for statistics, and the viable alternatives were also commercial – there was little choice but to fork over the money. Fortunately, there are now pretty viable alternatives for cash-strapped departments and individual researchers. There’s a lot of politics around the open-source movement, but for most people the important aspect is that the software is provided for free, and (generally) it’s cross-platform (or can be compiled to be so). All that’s required is to throw off the shackles of the evil capitalist oppressors, or something.
So, there’s a lot of software listed on my Links page but I thought I’d pick out my favourite bits of open-source software, that are most useful for researchers and students in psychology.
First up – general office-type software; there are a couple of good options here. The Open Office suite has been around for 20 years, and contains all the usual tools (word processor, presentation-maker, spreadsheet tool, and more). It’s a solid, well-designed system that can pretty seamlessly read and write the Microsoft Office XML-based (.docx, .pptx) file formats. The other option is Libre Office, which has the same roots as Open Office, and similar features. Plans are apparently underway to port Libre Office to iOS and Android – nice. The other free popular options for presentations is, of course, Prezi.
There are lots of options for graphics programs, however the two best in terms of features are without a doubt GIMP (designed to be a free alternative to Adobe Photoshop) and Inkscape (vector graphics editor – good replacement for Adobe Illustrator). There’s a bit of a steep learning curve for these, but that’s true of their commercial counterparts too.
Programming experiments – if you’re still using a paid system like E-Prime or Presentation, you should consider switching to PsychoPy – it’s user-friendly, genuinely cross-platform, and absolutely free. I briefly reviewed it before, here. Another excellent option is Open Sesame.
For statistical analysis there are a couple of options. Firstly, if you’re a SPSS-user and pretty comfortable with it (but fed up of the constant hassles of the licensing system), you should check out PSPP; a free stats program designed to look and feel like SPSS, and replicate many of the functions. You can even use your SPSS syntax – awesome. The only serious issue is that it doesn’t contain the SPSS options for complex GLM models (repeated measures ANOVA, etc.). Hopefully these will be added at some future point. The other popular option is the R language for statistical computing. R is really gaining traction at the moment. The command-line interface is a bit of a hurdle for beginners, but that can be mitigated somewhat by IDEs like R-Commander or RStudio.
For neuroscience there’s the NeuroDebian project – not just a software package, but an entire operating system, bundled with a comprehensive suite of neuroscience tools, including FSL, AFNI and PyMVPA, plus lots of others. There really are too many bits of open-source neuro-software to list here, but a good place to find some is NITRC.org.
So, there you are people; go open-source. You have nothing to lose but your over-priced software subscriptions.
Statistics – the very word is guaranteed to bring a shudder of terror to the average undergraduate, and even full-grown lecturers have been known to quake in fear before its awesome power. Most psychology undergraduates don’t come from a hard-science or mathematics background, and statistics are probably the number one thing that they struggle with during their psychology courses. Personally, I got through my undergraduate stats exams with a mixture of vague understanding and rote memorisation, and it was only during my PhD that I actually started learning how to do things properly and, more importantly, actually understanding what I was doing, and why.
This is not the place to give any detailed information on the basics of statistics. That kind of material has been covered many, many times before by people infinitely more qualified than I. For that kind of stuff, a good place to start would be Andy Field’s book, available here. Andy explains things very clearly and is actually a very nice chap as well. What I’d like to do instead is do a quick run-down of popular stats software, and point out some resources which can help if you run into trouble. Read the rest of this entry