Abstract
This perspective paper explores challenges associated with online crowdsourced data collection, particularly focusing on longitudinal tasks with time-sensitive outcomes like response latencies. Based on our research, we identify two significant sources of bias: technical shortcomings such as low, variable frame rates, and human factors, contributing to high attrition rates. We explored potential solutions to these problems, such as enforcing hardware acceleration and defining study-specific frame rate thresholds, as well as pre-screening participants and monitoring hardware performance and task engagement over each experimental session. With this discussion, we intend to provide recommendations on how to improve the quality and reliability of data collected via online crowdsourced platforms and emphasize the need for researchers to be cognizant of potential pitfalls in online research.