Spotted: design bias in sensor-based experience sampling methods

On possible biases in experience sampling with sensors

// published on Ubiquitous Computing-Latest Proceeding Volume // visit site

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

Neal Lathia, Kiran K. Rachuri, Cecilia Mascolo, Peter J. Rentfrow

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.