Experience sampling method

The experience sampling method, also referred to as a daily diary method, or ecological momentary assessment (EMA), is an intensive longitudinal research methodology that involves asking participants to report on their thoughts, feelings, behaviors, and/or environment on multiple occasions over time.[1] Participants report on their thoughts, feelings, behaviors, and/or environment in the moment (right then, not later; right there, not elsewhere) or shortly thereafter.[2] Participants can be given a journal with many identical pages. Each page can have a psychometric scale, open-ended questions, or anything else used to assess their condition in that place and time. ESM studies can also operate fully automatized on portable electronic devices or via the internet.[3] The experience sampling method was developed by Larson and Csikszentmihalyi.[4]

Overview

There are different ways to signal participants when to take notes in their journal or complete a questionnaire,[5] like using preprogrammed stopwatches. An observer can have an identically programmed stopwatch, so the observer can record specific events as the participants are recording their feelings or other behaviors. It is best to avoid letting subjects know in advance when they will record their feelings, so they can't anticipate the event, and will just be "acting naturally" when they stop and take notes on their current condition. Conversely, some statistical techniques require roughly equidistant time intervals, which has the limitation that assessments can be anticipated. Validity in these studies comes from repetition, so you can look for patterns, like participants reporting greater happiness right after meals. These correlations can then be tested by other means for cause and effect, such as vector autoregression,[6] since ESM just shows correlation.

Some authors also use the term experience sampling to encompass passive data derived from sources such as smartphones, wearable sensors, the Internet of Things, email and social media that do not require explicit input from participants.[7] These methods can be advantageous as they impose less demand on participants improving compliance and allowing data to be collected for much longer periods, are less likely to change the behaviour being studied and allow data to be sampled at much higher rates and with greater precision. Many research questions can benefit from both active and passive forms of experience sampling.

The first mobile device application that could be used as a tool for Experience Sampling Method was the ESP Package (dating to the late 1990s). This had limited functionality in that it is designed for older iOS Palm devices and had limited scheduling capabilities. It no longer works on modern mobile devices.[8] iHabit was the first smartphone mobile application designed for Experience Sampling. It was developed in 2011 and used in a study published by PLOS One in 2013.[9] In 2015, it was superseded by the LifeData system, which was used in a study published by JAMA Pediatrics in 2016.[10] This system has subsequently been used in numerous studies. The PIEL Survey app (first version 2012) is a free app available in iOS[11] and Android [12]versions and has since been used in more than 12 academic publications. It can be used for scheduled, random and on-demand surveys. Unlike many platforms, no server is required as data is saved on the device and emailed to the researcher or else retrieved by file sharing.[13] Other early smartphone platforms for ESM include SurveySignal[14] and Ilumivu (developed in 2012), MetricWire (developed in 2013), Instant Survey, Movisens, and Aware (Open Source). The largest ESM study was achieved through PSYT's Mappiness App,[15] PSYT’s apps collect data through ESM as well as reporting the data back to users to enable real-time visualisation and tracking of variables. Several other commercial and open source systems are currently available to help researchers run ESM studies,[16] including BeepMe,[17] and Expimetrics.[18] Physiqual enables researchers to gather and integrate data from commercially available sensors and service providers to use them in ESM,[19] including Fitbit and Google Fit. As of 2014, Movisens have developed the ability to trigger sampling forms from physiological data such as actigraphy and ECG.[20] unforgettable.me provide a platform for both active and passive experience sampling that allows the integration of some 400 data sources.

See also

References
  1. Bolger, N.; Laurenceau, J.P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York, N.Y.: Guilford Press.
  2. Csikszentmihalyi, M. (July 2014). Validity and Reliability of the Experience-Sampling Method. New York: Springer. p. 322. ISBN 978-94-017-9087-1.
  3. van der Krieke; et al. (2015). "HowNutsAreTheDutch (HoeGekIsNL): A crowdsourcing study of mental symptoms and strengths" (PDF). International Journal of Methods in Psychiatric Research. 25 (2): 123–144. doi:10.1002/mpr.1495. PMC 6877205. PMID 26395198.
  4. Larson, R.; Csikszentmihalyi, M. (1983). "The experience sampling method". New Directions for Methodology of Social and Behavioral Science. 15: 41–56.
  5. Hektner, J.M., Schmidt, J.A., Csikszentmihalyi, M. (Eds.). (2006). Experience Sampling Method: Measuring the Quality of Everyday Life. Sage Publications, Inc. ISBN 978-1-4129-2557-0
  6. van der Krieke, L; Blaauw, FJ; Emerencia, AC; Schenk, HM; Slaets, JP; Bos, EH; de Jonge, P; Jeronimus, BF (2016). "Temporal Dynamics of Health and Well-Being: A Crowdsourcing Approach to Momentary Assessments and Automated Generation of Personalized Feedback (2016)". Psychosomatic Medicine. 79 (2): 213–223. doi:10.1097/PSY.0000000000000378. PMID 27551988.
  7. Nielson, D. M.; Smith, T. A.; Sreekumar, V.; Dennis, S.; Sederberg, P. B. (2015). "Human hippocampus represents space and time during retrieval of real-world memories". Proceedings of the National Academy of Sciences. 112 (35): 11078–11083. Bibcode:2015PNAS..11211078N. doi:10.1073/pnas.1507104112. PMC 4568259. PMID 26283350.
  8. Mehl, Matthias J.; Tamlin, Conner L. (2013-10-01). Handbook of Research Methods for Studying Daily Life. ISBN 9781462513055.
  9. Runyan, J. D.; Steenbergh, T. A.; Bainbridge, C.; Daugherty, D. A.; Oke, L.; Fry, B. N. (2013). "A smartphone ecological momentary assessment/intervention "app" for collecting real-time data and promoting self-awareness". PLOS ONE. 8 (8): e71325. Bibcode:2013PLoSO...871325R. doi:10.1371/journal.pone.0071325. PMC 3743745. PMID 23977016.
  10. Wiebe, Douglas J.; Nance, Michael L.; Houseknecht, Eileen; Grady, Matthew F.; Otto, Nicole; Sandsmark, Danielle K.; Master, Christina L. (2016). "Ecologic Momentary Assessment to Accomplish Real-Time Capture of Symptom Progression and the Physical and Cognitive Activities of Patients Daily Following Concussion". JAMA Pediatrics. 170 (11): 1108–1110. doi:10.1001/jamapediatrics.2016.1979. PMID 27617669.
  11. "PIEL Survey".
  12. "PIEL Survey - Apps on Google Play".
  13. https://pielsurvey.org/profile/survey-experience-sampling-method/
  14. Hofmann, W., & Patel, P. V. (2015). SurveySignal: A convenient solution for experience sampling research using participants’ own smartphones. Social Science Computer Review, 33, 235-253. http://journals.sagepub.com/doi/pdf/10.1177/0894439314525117
  15. "Archived copy" (PDF). Archived from the original (PDF) on 2016-07-05. Retrieved 2016-11-11.CS1 maint: archived copy as title (link)
  16. Conner, T. S. (2013, May). Experience sampling and ecological momentary assessment with mobile phones. Retrieved from http://www.otago.ac.nz/psychology/otago047475.pdf
  17. as available through, e.g., F-Droid catalogue Archived 2016-03-06 at the Wayback Machine
  18. "Expimetrics".
  19. Blaauw; et al. (2016). "Let's get Physiqual - an intuitive and generic method to combine sensor technology with ecological momentary assessments". Journal of Biomedical Informatics. 63: 141–149. doi:10.1016/j.jbi.2016.08.001. PMID 27498066.
  20. "Interactive Ambulatory Assessment - Project - movisens GmbH".
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.