Journal article

Modeling interaction as a complex system

Niels van Berkel, Simon Dennis, Michael Zyphur, Jinjing Li, Andrew Heathcote, Vassilis Kostakos

Human-Computer Interaction | Taylor & Francis | Published : 2020

Abstract

Researchers in Human-Computer Interaction typically rely on experiments to assess the causal effects of experimental conditions on variables of interest. Although this classic approach can be very useful, it offers little help in tackling questions of causality in the kind of data that are increasingly common in HCI – capturing user behavior ‘in the wild.’ To analyze such data, model-based regressions such as cross-lagged panel models or vector autoregressions can be used, but these require parametric assumptions about the structural form of effects among the variables. To overcome some of the limitations associated with experiments and model-based regressions, we adopt and extend ‘empirical..

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Grants

Awarded by ARC Discovery Project


Awarded by Australian Research Council [ARC Discovery Project]


Funding Acknowledgements

This work is partially funded by a Samsung Global Research Outreach grant, and the ARC Discovery Project [DP190102627]; Australian Research Council [ARC Discovery Project DP190102627]; Samsung [Samsung Global Research Outreach grant].