Senior Research Associate, Lancaster University
Heather holds a BSc in Psychology and is studying how digital traces of behaviour can infer individual differences about a user. Within this she has explored how data can be objectively message via logging technology use and has developed theoretical ideas to explain the underpinnings of technology usage.
Also of interest is the exploration of new analysis methods when quantifying human behaviour, including machine learning approaches such as decision tree modelling. Heather is interested in out-of-the-lab solutions, measuring behaviour “in-situ” using smartphone applications or other mobile technology. Her work in CREST will explore group dynamics including how to disrupt cohesion of criminal groups and the analysis of language using computational approaches such as LIWC.
Wilcockson, T. D. W., Ellis, D. A. and Shaw, H. (2018) Determining typical smartphone usage: What data do we need? Cyberpsychology, Behavior and Social Networking, 21,6,395-398. https://doi.org/10.1089/cyber.2017.0652
Shaw, H., Ellis, D.A., & Ziegler, F.V. (2018). The Technology Integration Model (TIM). Predicting the continued use of technology. Computers in Human Behavior, 83, 204-214. https://doi.org/10.1016/j.chb.2018.02.001
Shaw, H., Ellis, D.A., Kendrick, L., Ziegler, F.V. & Wiseman, R. (2016). Predicting smartphone operating system from personality and individual differences. Cyberpsychology, Behavior, and Social Networking, 19(12), 727-732. https://doi.org/10.1089/cyber.2016.0324
Andrews, S., Ellis, D. A., Shaw. & Piwek, L. (2015). Beyond Self-Report: Tools to compare estimates and real world smartphone use. PLOS One, 10(10), e0139004. https://doi.org/10.1371/journal.pone.0139004