Lately, I've gotten some requests for a tutorial on deploying jsPsych experiments, so I've committed to writing one. It will be slightly better suited for people with some programming experience, but if you're not familiar with the web and the terminology circulating around the technologies I'm using, please don't be intimidated. My intention for this tutorial is to write a solution that is sufficiently simple so that you should be able to deploy your jsPsych experiment with no previous back end experience.

For my previous study, with minimal recruitment, I had a total of 290 participants from a very specific population, in just a few days of open recruitment. The completion rate was at over 56%, which is amazing when you consider that the 20-30 minutes of participation consisted of tasks that require constant vigilance and mental effort and were really no walk in the park, and definitely not entertaining. Despite of being meant to assess cognitive skills and team cohesion in League of Legends players, the experiment served another purpose - to assess the rate of engagement in what I like to refer to as native experiments.

Some participants are excited about your research, but they drop out when they're disappointed with the execution. The first thing you need to do is to polish your user experience to a degree where there is nothing intrinsic to the design that would irritate the user sufficiently to cause them to quit. It is possible that the user will still drop out because the battery is too long or because the task is too difficult, but if they drop out because your instructions are poorly written or because your web page lacks usability, you lose a participant for absolutely no reason.

One of the problems cited for web-based experiments is a relatively high rate of participant drop out. In the next three blog posts, we're going to discuss how to minimize drop out and how to maximize participant engagement by using some very simple principles and good habits from product development and customer service. We'll also come up with some more creative ideas on how to keep participants interested and even how to leverage virality to access more participants.

Recently, I programmed a set of surveys and cognitive tasks for my master's thesis. For some of the tasks, I used jsPsych - a JavaScript-library for creating web-based experiments, for the back-end I chose Node.js with Express 4.0 and Sequelize. I deployed the experiment on DigitalOceans, and I describe my experience here. If you'd be interested in a series of tutorials on how to deploy jsPsych experiments, throw me an email!

Web experiments are excellent for maximizing the scope of a study when it comes to the participant pool. Utilizing web experiments, one can expect very large and diverse samples, which can open doors to addressing hypotheses that simply could not be tested in a lab environment. But as always, this major advantage has to be weighted and considered in light of the shortcomings and challenges of setting up and distributing web-based assessment systems. The shortcomings of web-based systems are the most dire when it comes to accurate timing of stimulus presentation and response logging.

I recently had a talk about the state of open-source programmable systems for generating experiments for behavioral research. I decided to write up a series of blog posts capturing the most important points of my presentation, involving introductions on a few browser-based systems for creating experiments, a discussion on the challenges and benefits of these web-based solutions, and how the development of programmable behavioral experiments could be improved.

I have been working through a book on Freemium Economics, by Eric Benjamin Seufert: “Freemium Economics – leveraging analytics and user segmentation to drive revenue: the savvy manager's guide” The book presents the idea on how to leverage analytics and user segmentation to drive revenue in a business, where the revenue is generated only by a fraction of the user base. The book sparked a wider curiosity towards business analytics that I have been satisfying by going through online courses on topics ranging from Web analytics to databases and map reduce.

I spent some time writing a report – although mostly for my own needs – about the main characteristics of some of the most popular children’s game apps on the iOS market, and on the values and characteristics the most prominent companies developing apps for children seem to capitalize on. Reflecting on the topic, I noted a few popular features in children’s apps and games from the most popular publishers.

Over time, the blog will cover a colorful range of topics, from game studies and web experiments to usability and AI, but as a general topic, I will be writing about the role of psychology in information technology (and vice versa). Typically an initial response from people to the idea is befuddlement, as amazingly many of them think that people and computers don’t mix – I heard this a lot during the year I studied computer science on the side of psychology. I wrote this post to disentangle some of the misconceptions.