Abstract
When running experiments within the field of Human Computer Interaction (HCI) it is common practice to ask participants to come to a specified lab location, and reimburse them monetarily for their time and travel costs. This, however, is not the only means by which to encourage participation in scientific study. Citizen science projects, which encourage the public to become involved in scientific research, have had great success in getting people to act as sensors to collect data or to volunteer their idling computer or brain power to classify large data sets across a broad range of fields including biology, cosmology and physical and environmental science. This is often done without the expectation of payment. Additionally, data collection need not be done on behalf of an external researcher; the Quantified Self (QS) movement allows people to reflect on data they have collected about themselves. This too, then, is a form of non-reimbursed data collection. Here we investigate whether citizen HCI scientists and those interested in personal data produce reliable results compared to participants in more traditional lab-based studies. Through six studies, we explore how participation rates and data quality are affected by recruiting participants without monetary reimbursement: either by providing participants with data about themselves as reward (a QS approach), or by simply requesting help with no extrinsic reward (as in citizen science projects). We show that people are indeed willing to take part in online HCI research in the absence of extrinsic monetary reward, and that the data generated by participants who take part for selfless reasons, rather than for monetary reward, can be as high quality as data gathered in the lab and in addition may be of higher quality than data generated by participants given monetary reimbursement online. This suggests that large HCI experiments could be run online in the future, without having to incur the equally large reimbursement costs alongside the possibility of running experiments in environments outside of the lab.
Highlights
Experiments in the field of Human Computer Interaction (HCI) can help researchers evaluate the way that people interact with devices
In this paper we present a report of our attempts to run a 2 x 3 condition typing study conducted in order to test the effectiveness of alternative reimbursement methods in HCI research
The success of the Quantified Self and Citizen Science recruitment in other fields suggests that such approaches may be beneficial for HCI research
Summary
Experiments in the field of Human Computer Interaction (HCI) can help researchers evaluate the way that people interact with devices. The issue with such an approach is that it is impossible to understand fully the vast array of elements affecting the interactions they are studying; with a large number of confounding factors it can be hard to pin point causality between the technology and behavior By moving these interactions into the lab with a designated task to complete, a wide array of elements can be controlled, meaning the researcher is able to manipulate solely the aspect they wish to study, whilst keeping other features fixed. A combination of these results can be statistically analysed to determine whether the researcher-controlled variations within the task significantly affected the user interaction with the device or task being studied
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