IFTTT, if this then that, is an online, multi-service task automation tool I first read about on Lifehacker last year. I finally started using it today, and am seriously impressed.
Once you’ve signed up for an account, you can create IFTTT ‘recipes’ to check for actions and events on one online service (e.g. Google Reader, Dropbox, WordPress, Facebook etc.) and use them as an automatic trigger of a predetermined action in another (e.g. Gmail, Google Calendar, Tumblr etc.)
Example: To keep track of journal articles I should read, I monitor journal table of contents RSS feeds and e-mail interesting posts to myself for later download and consumption. I use my iPad, my phone, and occasionally my PC browser to access Google Reader, but struggle with how fiddly it is to e-mail myself on my mobile devices (with my filter-trigger keywords in the message body) whenever I find an article I want to read. I’m sure I’ve missed articles I ought to have read through setting my action criterion a bit too high, as a direct result of how annoying it is to e-mail myself articles using the various Google Reader interfaces on my mobile devices. Today I set up IFTTT to check for starred Google Reader feed items, and automatically do everything else beyond this that I find annoying. Perfect!
IFTTT will check for custom recipe triggers every 15 minutes, so it isn’t something you’d want to use for actions you require to be instantaneous, but it’s perfect for situations like the above. The services with which it is integrated are many and varied, and the possibilities therefore nearly limitless.
UPDATE 16/04/2014: I just came across this page and found that I had referenced the now defunct Google Reader. When Reader died I moved all of my RSS feeds across to feedly, which IFTTT supports with identical functionality. I also apply the same rule to twitter posts I favourite, meaning that I have a Gmail folder in which IFTTT aggregates all of the stuff I want to read from both feedly and twitter.
On top of the study completion data that’s obvious from the 7 KB csv file that each happily-debriefed participant leaves behind, the Google Analytics code embedded in each page of the experiment provides further opportunity to explore participation data.
As the experiment structure is entirely linear, it’s possible to track the loss of participants from each page to the next.
The major point of attrition is between the Participant Information Page and the Consent Form – not surprising given quite how text-heavy the first page was, and how ‘scary’ headings like “Are there any potential risks to taking part?” make the study sound. The content of that first page is entirely driven by the Informed Consent requirements of the University of St Andrews, but the huge attrition rate here has prompted a bit of a redesign in the next follow-up study.
Other information useful for the design of future studies has been the browser data. As might be expected, Firefox and its relatives are the dominant browsers, with Chrome a distant second and Internet Explorer lagging far behind. Implementing fancy HTML5 code that won’t work in Firefox is therefore a bad idea. On top of that, despite how tablet- and phone-friendly the experiment was, very few people used this sort of device to complete the study – it’s probably a waste of time optimising the site specifically for devices like iPads.
Curiously enough, when the data for study completions are explored by browser, the three major platforms start to level up. Chrome, Firefox and IE all yield similar completion statistics, suggesting that IE browsers are far more likely to follow through and complete the study once they visit the site. I’m speculating here, but I suspect that this has something to do with a) this being a memory study and b) IE being used by an older demographic of internet user who may be interested in how they perform. Of the three major browsers, Firefox users have the worst completion rate.
Another consideration with word-based experiments is the location of participants. This could impact on the choice of words used in future studies (American or UK spellings) and could be considered important by those who are keen to exclude those who don’t speak English as their first language. Finer grained information about participants’ first languages is something we got from participant self-reports in the demographic questionnaire, but the table of new visits and study completions is still rather interesting.
Once again, there are few surprises here, with the US dominating the new visits list, though one new visit from a UK- or India-based browser is more likely to lead to a study completion. A solid argument for using North American spellings and words could also be made from these data.
Source of Traffic
The most important thing to do to make potential participants aware of an online psychology study is to advertise it. But where?
While getting the study listed on stumbleupon was a real coup, it didn’t lead to very many study completions (a measly 2.5%). That’s not surprising – the study doesn’t capture the attention from page 1 and doesn’t have much in the way of internet meme-factor. That is, of course, something that we should be rectifying in future studies if we want them to go viral, but it’s tough to do within the rigid constraints of the informed consent pages that must precede the study itself.
These statistics, surprised me more than any other. I assumed that social networking, not a dedicated experiment listing page, would be how people would find the study. But in retrospect, it all makes sense. There is clearly a large number of people out there who want to do online psychology studies, and what better way to find them than to use a directory that lists hundreds of them. If there’s one place you should advertise your online studies, it’s psych.hanover.edu.
I think the Raspberry Pi is going to be fantastic, for reasons summed up very nicely by David McGloin – the availability of such a cheap and versatile barebones technology will kickstart a new generation of tinkerers and coders.
It’s worth mentioning that this kickstart wouldn’t just be limited to the newest generation currently going through their primary and secondary school educations. Should my hands-on experience of the device live up to my expectations (and the expectations of those who have bought all the units that went on sale this morning), I will be ordering a couple for each PhD student I take on. After all, what’s the point in using an expensive desktop computer running expensive software on an expensive OS to run simple psychology experiments that have hardly changed in the past 15 years? What’s the point when technology like the Raspberry PI is available for £22? Moreover, if you can get researchers to present experiments using a medium that has also helped them pick up some of the most desirable employment skills within and outwith academia, expertise with and practical experience in programming, then I think that’s a fairly compelling argument that it would be irresponsible not to.
But won’t I have missed a critical period in my students’ development from technology consumers into technology hackers?
Every psychology student can and should learn how to code (courtesy of Matt Wall), and it’s never too late. I learned to code properly in my twenties, during my postdoc. The reason it took me so long was that I had never needed to code in any serious goal-driven way before this time. Until the end of my PhD, Superlab and E-Prime had been perfectly passable vehicles by which I could present my experiments to participants. My frustration with the attempts of these experiment presentation packages to make things ‘easy’, which ended up making things sub-optimal, led me to learn how to use the much ‘harder’ Matlab and Psychophysics Toolbox to present my experiments. Most importantly, I was given license to immerse myself in the learning process by my boss. This is what I hope giving a PhD student a couple of Raspberry Pis will do, bypassing the tyranny of the GUI-driven experiment design package in the process. Short-term pain, long-term gain.
In a few years, my behavioural testing lab-space could simply be a number of rooms equipped with HDMI monitors, keyboards and mice. Just before testing participants, students and postdocswill connect these peripherals to their own code-loaded Raspberry Pis, avoiding the annoyances of changed hardware settings, missing dongles and unre
liable network licenses. It could be brilliant, but whatever it is, it will be cheap.