What over 1,000,000 participants tell us about online research protocols.

With the ever-increasing adoption of tools for online research, for the first time we have visibility on macro-level trends in research that were previously unattainable. However, until now this data has been siloed within company databases and unavailable to researchers. Between them, the online study creation and hosting tool Gorilla Experiment Builder and the recruitment platform Prolific hold metadata gleaned from millions of participants and over half a million studies. We analyzed a subset of this data (over 1 million participants and half a million studies) to reveal critical information about the current state of the online research landscape that researchers can use to inform their own study planning and execution. We analyzed this data to discover basic benchmarking statistics about online research that all researchers conducting their work online may be interested to know. In doing so, we identified insights related to: the typical study length, average completion rates within studies, the most frequent sample sizes, the most popular participant filters, and gross participant activity levels. We present this data in the hope that it can be used to inform research choices going forward and provide a snapshot of the current state of online research.

Realistic precision and accuracy of online experiment platforms, web browsers, and devices.

Due to increasing ease of use and ability to quickly collect large samples, online behavioural research is currently booming. With this popularity, it is important that researchers are aware of who online participants are, and what devices and software they use to access experiments. While it is somewhat obvious that these factors can impact data quality, the magnitude of the problem remains unclear. To understand how these characteristics impact experiment presentation and data quality, we performed a battery of automated tests on a number of realistic set-ups. We investigated how different web-building platforms (Gorilla v.20190828, jsPsych v6.0.5, Lab.js v19.1.0, and psychoJS/PsychoPy3 v3.1.5), browsers (Chrome, Edge, Firefox, and Safari), and operating systems (macOS and Windows 10) impact display time across 30 different frame durations for each software combination. We then employed a robot actuator in realistic set-ups to measure response recording across the aforementioned platforms, and between different keyboard types (desktop and integrated laptop). Finally, we analysed data from over 200,000 participants on their demographics, technology, and software to provide context to our findings. We found that modern web platforms provide reasonable accuracy and precision for display duration and manual response time, and that no single platform stands out as the best in all features and conditions. In addition, our online participant analysis shows what equipment they are likely to use.

Gorilla in our midst: An online behavioral experiment builder.

Behavioral researchers are increasingly conducting their studies online, to gain access to large and diverse samples that would be difficult to get in a laboratory environment. However, there are technical access barriers to building experiments online, and web browsers can present problems for consistent timing-an important issue with reaction-time-sensitive measures. For example, to ensure accuracy and test-retest reliability in presentation and response recording, experimenters need a working knowledge of programming languages such as JavaScript. We review some of the previous and current tools for online behavioral research, as well as how well they address the issues of usability and timing. We then present the Gorilla Experiment Builder (gorilla.sc), a fully tooled experiment authoring and deployment platform, designed to resolve many timing issues and make reliable online experimentation open and accessible to a wider range of technical abilities. To demonstrate the platform's aptitude for accessible, reliable, and scalable research, we administered a task with a range of participant groups (primary school children and adults), settings (without supervision, at home, and under supervision, in both schools and public engagement events), equipment (participant's own computer, computer supplied by the researcher), and connection types (personal internet connection, mobile phone 3G/4G). We used a simplified flanker task taken from the attentional network task (Rueda, Posner, & Rothbart, 2004). We replicated the "conflict network" effect in all these populations, demonstrating the platform's capability to run reaction-time-sensitive experiments. Unresolved limitations of running experiments online are then discussed, along with potential solutions and some future features of the platform.