Advancement in digital technologies and sensors is enabling data to be collected from actual physical day-to-day interactions in addition to activities on the Internet. There is now data from spending and social media postings, music listens on Spotify, Fitbit sleeps and activities, all of which provide opportunities to observe and analyse authentic human behaviours through data, without being intrusive. How could such data empower the development of research projects and new scientific research tools and methods in social sciences? This is the question we pose in our study as we craft the new methodology on Behavioural Visibility in Data (BeViD).
The ability for individuals to share their own data has only been possible recently, with the HAT (the Hub of All Things) technology that enables individuals to collect their own data from a range of Internet services and IoT devices and then donate or exchange that data. The BeViD method includes the steps of any scientific process: observation, developing hypothesis, making predictions as well as testing the predictions through analysis of other individual BeVID records, and finally, developing theory.
The BeVID project aims to develop a robust methodological approach in using HATs for research. It will document how to set up respondent panels, create rules and put in place tests for reliability, and validity. It also investigates classification algorithms (based on decision-trees, neural networks or fuzzy logic techniques) to ensure higher levels of accuracy in monitoring relevant behavioural activities. The research rigour of the new BeViD methodology must ensure unbiased experimental design, analysis, interpretation, and reporting of results so that it can be a significant contribution to research and society.