MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Cauldron Science, St Johns Innovation Centre, Cambridge, UK.
Behav Res Methods. 2021 Aug;53(4):1407-1425. doi: 10.3758/s13428-020-01501-5. Epub 2020 Nov 2.
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.
由于使用越来越方便,并且能够快速收集大量样本,在线行为研究目前正在蓬勃发展。随着这种普及,研究人员了解在线参与者是谁以及他们使用什么设备和软件来访问实验变得非常重要。虽然这些因素会影响数据质量是显而易见的,但问题的严重程度尚不清楚。为了了解这些特征如何影响实验呈现和数据质量,我们在许多现实设置中进行了一系列自动化测试。我们研究了不同的网页构建平台(Gorilla v.20190828、jsPsych v6.0.5、Lab.js v19.1.0 和 psychoJS/PsychoPy3 v3.1.5)、浏览器(Chrome、Edge、Firefox 和 Safari)以及操作系统(macOS 和 Windows 10)如何影响每种软件组合在 30 个不同帧持续时间下的显示时间。然后,我们在现实设置中使用机器人执行器来测量上述平台以及不同键盘类型(台式机和集成笔记本电脑)之间的响应记录。最后,我们分析了超过 200,000 名参与者的人口统计学、技术和软件数据,为我们的发现提供了背景信息。我们发现,现代网页平台为显示持续时间和手动响应时间提供了合理的准确性和精度,并且没有一个平台在所有功能和条件下都表现出色。此外,我们的在线参与者分析显示了他们可能使用的设备。