On the state of reporting in crowdsourcing experiments and a checklist to aid current practices
This addresses reporting gaps in crowdsourcing experiments for researchers, though it is incremental as it builds on existing guidelines.
The paper examined the current state of reporting in crowdsourcing experiments, analyzing 171 experiments to identify under-reported implementation choices that affect reproducibility, and proposed a checklist to improve reporting practices.
Crowdsourcing is being increasingly adopted as a platform to run studies with human subjects. Running a crowdsourcing experiment involves several choices and strategies to successfully port an experimental design into an otherwise uncontrolled research environment, e.g., sampling crowd workers, mapping experimental conditions to micro-tasks, or ensure quality contributions. While several guidelines inform researchers in these choices, guidance of how and what to report from crowdsourcing experiments has been largely overlooked. If under-reported, implementation choices constitute variability sources that can affect the experiment's reproducibility and prevent a fair assessment of research outcomes. In this paper, we examine the current state of reporting of crowdsourcing experiments and offer guidance to address associated reporting issues. We start by identifying sensible implementation choices, relying on existing literature and interviews with experts, to then extensively analyze the reporting of 171 crowdsourcing experiments. Informed by this process, we propose a checklist for reporting crowdsourcing experiments.