research-article

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

Authors:

Kiran K. Rachuri,

Cecilia Mascolo,

Peter J. RentfrowAuthors Info & Claims

UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing

Pages 183 - 192

https://doi.org/10.1145/2493432.2493452

Published: 08 September 2013 Publication History

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.

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Cited By

Brombacher HDritsa DVos SHouben S(2024)"To Click or not to Click": Back to Basic for Experience Sampling for Office Well-being in Shared Office SpacesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642295(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642295
Zhang GZhao PZhang AZhang GZhao PZhang A(2024)Synthesizing Privacy-Preserving Traces: Enhancing Plausibility with Social NetworksPrivacy Preservation in Distributed Systems10.1007/978-3-031-58013-0_2(25-52)Online publication date: 8-Apr-2024
https://doi.org/10.1007/978-3-031-58013-0_2
Reiter TSchoedel R(2023)Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studiesBehavior Research Methods10.3758/s13428-023-02252-956:4(4038-4060)Online publication date: 6-Nov-2023
https://doi.org/10.3758/s13428-023-02252-9
Show More Cited By

Index Terms

Contextual dissonance: design bias in sensor-based experience sampling methods
1. Information systems

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cover image ACM Conferences

UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing

September 2013

846 pages

ISBN:9781450317702

DOI:10.1145/2493432

General Chairs:
Friedemann Mattern
ETH Zurich, CH
,
Silvia Santini
TU Darmstadt, DE
,
Program Chairs:
John F. Canny
UC Berkeley, US
,
Marc Langheinrich
Università della Svizzera italiana, CH
,
Jun Rekimoto
University of Tokyo, JP

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
University of Florida: University of Florida
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 08 September 2013

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UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

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UbiComp '13 Paper Acceptance Rate 92 of 394 submissions, 23%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Brombacher HDritsa DVos SHouben S(2024)"To Click or not to Click": Back to Basic for Experience Sampling for Office Well-being in Shared Office SpacesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642295(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642295
Zhang GZhao PZhang AZhang GZhao PZhang A(2024)Synthesizing Privacy-Preserving Traces: Enhancing Plausibility with Social NetworksPrivacy Preservation in Distributed Systems10.1007/978-3-031-58013-0_2(25-52)Online publication date: 8-Apr-2024
https://doi.org/10.1007/978-3-031-58013-0_2
Reiter TSchoedel R(2023)Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studiesBehavior Research Methods10.3758/s13428-023-02252-956:4(4038-4060)Online publication date: 6-Nov-2023
https://doi.org/10.3758/s13428-023-02252-9
Lee HCheng YWang WLin CHuang Y(2023)Exploring the Learning Process and Effectiveness of STEM Education via Learning Behavior Analysis and the Interactive-Constructive- Active-Passive FrameworkJournal of Educational Computing Research10.1177/0735633122113688861:5(951-976)Online publication date: 30-Jan-2023
https://doi.org/10.1177/07356331221136888
Lee HHooshyar MLin CWang WHuang YHong JLanperne MPark JCerny TShahriar H(2023)Image4Assess: Automatic learning processes recognition using image processingProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577643(11-16)Online publication date: 27-Mar-2023
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Ruensuk MKim THong HOakley I(2022)Sad or just jealous? Using Experience Sampling to Understand and Detect Negative Affective Experiences on InstagramProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517561(1-18)Online publication date: 29-Apr-2022
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van Berkel ND’Alfonso SKurnia Susanto RFerreira DKostakos V(2022)AWARE-Light: a smartphone tool for experience sampling and digital phenotypingPersonal and Ubiquitous Computing10.1007/s00779-022-01697-727:2(435-445)Online publication date: 5-Nov-2022
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Gutierrez LCastro LBanos O(2022)Challenges and Opportunities for Designing Technology-Based Ecological Momentary Interventions (EMIs) in Mental HealthProceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022)10.1007/978-3-031-21333-5_88(888-899)Online publication date: 21-Nov-2022
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Lee HChang WHuang Y(2022)Combining Deep Learning and Computer Vision Techniques for Automatic Analysis of the Learning Process in STEM EducationInnovative Technologies and Learning10.1007/978-3-031-15273-3_3(22-32)Online publication date: 29-Aug-2022
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Khanshan AVan Gorp PNuijten RMarkopoulos P(2021)Assessing the Influence of Physical Activity Upon the Experience Sampling Response Rate on Wrist-Worn DevicesInternational Journal of Environmental Research and Public Health10.3390/ijerph18201059318:20(10593)Online publication date: 10-Oct-2021
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