Contextual dissonance: design bias in sensor-based experience sampling methods

Authors:
Neal Lathia;Kiran K. Rachuri;Cecilia Mascolo;Peter J. Rentfrow
Affiliations:
University of Cambridge, Cambridge, United Kingdom;University of Cambridge, Cambridge, United Kingdom;University of Cambridge, Cambridge, United Kingdom;University of Cambridge, Cambridge, United Kingdom
Venue:
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Year:
2013

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Abstract

The Experience Sampling Method (ESM) has been widely used to collect longitudinal survey data from participants; in this domain, smartphone sensors are now used to augment the context-awareness of sampling strategies. In this paper, we study the effect of ESM design choices on the inferences that can be made from participants' sensor data, and on the variance in survey responses that can be collected. In particular, we answer the question: are the behavioural inferences that a researcher makes with a trigger-defined subsample of sensor data biased by the sampling strategy's design? We demonstrate that different single-sensor sampling strategies will result in what we refer to as contextual dissonance: a disagreement in how much different behaviours are represented in the aggregated sensor data. These results are not only relevant to researchers who use the ESM, but call for future work into strategies that may alleviate the biases that we measure.