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.
- Barrett, L. F., and Barrett, D. J. An Introduction to Computerized Experience Sampling in Psychology. Social Science Computer Review 19, 2 (2001), 175--185. Google ScholarDigital Library
- Bolger, N., Davis, A., and Rafaeli, E. Diary Methods: Capturing Life as it is Lived. Annu. Rev. Psychology (2003).Google Scholar
- Carter, S., Mankoff, J., and Heer, J. Momento: Support for Situated Ubicomp Experimentation. In ACM CHI (San Jose, California, 2007). Google ScholarDigital Library
- Clark, L., and Watson, D. Mood and the Mundane: Relations Between Daily Life and Self-Reported Mood. Journal of Personality and Social Psychology 54, 2 (1988), 296--308.Google ScholarCross Ref
- Consolvo, S., McDonald, D., Toscos, T., Chen, M., Froehlich, J., Harrison, B., Klasnja, P., LaMarca, A., LeGrand, L., Libby, R., Smith, I., and Landay, J. Acitivity Sensing in the Wild: A Field Trial of Ubifit Garden. In ACM CHI (Florence, Italy, 2008). Google ScholarDigital Library
- Csikszentmihalyi, M., and LeFevre, J. Optimal Experience in Work and Leisure. Journal of Personality and Social Psychology 56, 5 (1989), 815--822.Google ScholarCross Ref
- Diener, E., and Emmons, R. The Independence of Positive and Negative Affect. Journal of Personality and Social Psychology 47 (1984).Google Scholar
- Eagle, N., and Pentland, A. Reality Mining: Sensing Complex Social Systems. Personal and Ubiquitous Computing 10 (2006), 255--268.Google ScholarDigital Library
- Froehlich, J., Chen, M. Y., Consolvo, S., Harrison, B., and Landay, J. MyExperience: A System for In situ Tracing and Capturing of User Feedback on Mobile Phones. In ACM MobiSys (Puerto Rico, 2007). Google ScholarDigital Library
- Froehlich, J., Dillahunt, T., Klasnja, P., Mankoff, J., Consolvo, S., Harrison, B., and Landay, J. UbiGreen: Investigating a Mobile Tool for Tracking and Supporting Green Transportation Habits. In ACM CHI (Boston, USA, 2009). Google ScholarDigital Library
- Intille, S., Rondoni, J., Kukla, C., Ancona, I., and Bao, L. Context-Aware Experience Sampling. In ACM CHI Extended Abstracts (Ft. Lauderdale, Florida, 2003). Google ScholarDigital Library
- Juslin, P., Liljestrom, S., Vastfjall, D., Barradas, G., and Silva, A. An Experience Sampling Study of Emotional Reactions to Music: Listener, Music, and Situation. Emotion 8, 5 (2008), 668--683.Google ScholarCross Ref
- Killingsworth, M., and Gilbert, D. A Wandering Mind is an Unhappy Mind. Science 330 (2010).Google Scholar
- Lathia, N., Rachuri, K., Mascolo, C., and Roussos, G. Open Source Smartphone Libraries for Computational Social Science. In 2nd ACM Workshop on Mobile Systems for Computational Social Science (Zurich, Switzerland, 2013). Google ScholarDigital Library
- Mackerron, G. Happiness and Environmental Quality. PhD Thesis, The London School of Economics and Political Science (2012).Google Scholar
- Mehl, M., and Conner, T., Eds. Handbook of Research Methods for Studying Daily Life. The Guildford Press, 2012.Google Scholar
- Mehl, M., Robbins, M., and Deters, F. Naturalistic Observation of Health-Relevant Social Processes: The Electronically Activated Recorder (EAR) Methodology in Psychosomatics. Psychosomatic Medicine 74 (2012), 410--417.Google ScholarCross Ref
- Miluzzo, E., Lane, N., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S., Zheng, X., and Campbell, A. Sensing Meets Mobile Social Networks: The Design, Implementation and Evaluation of the CenceMe Application. In ACM SenSys (Raleigh, NC, 2008). Google ScholarDigital Library
- Nezlek, J. B., Vansteelandt, K., Mechelen, I., and Kuppens, P. Appraisal-Emotion Relationships in Daily Life. Emotion 8, 1 (2008), 145--150.Google Scholar
- Oliver, E. A., and Keshav, S. An Empirical Approach to Smartphone Energy Level Prediction. In ACM Ubicomp (Beijing, China, 2011). Google ScholarDigital Library
- Patrick, K., Griswold, W., Raab, F., and Intille, S. Health and the Mobile Phone. American Journal of Preventive Medicine 35 (2008).Google Scholar
- Rachuri, K., Mascolo, C., Musolesi, M., and Rentfrow, P. Sociable Sense: Exploring the Trade-Offs of Adaptive Sampling and Computation Offloading for Social Sensing. In ACM MobiCom (Las Vegas, USA, 2011). Google ScholarDigital Library
- Rachuri, K., Musolesi, M., Mascolo, C., Rentfrow, P. J., Longworth, C., and Aucinas, A. Emotion Sense: A Mobile Phones based Adaptive Platform for Experimental Social Psychology Research. In ACM UbiComp (Copenhagen, Denmark, 2010). Google ScholarDigital Library
- Stone, A., and Shiffman, S. Ecological Momentary Assessment (EMA) in Behavioral Medicine. Annals of Behavioral Medicine 16, 3 (1994).Google ScholarCross Ref
Index Terms
- Contextual dissonance: design bias in sensor-based experience sampling methods
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