Opening the ‘Black Box’: An Overview of Methods to Investigate the Decision-Making Process in Choice-Based Surveys
- 184 Downloads
The desire to understand the preferences of patients, healthcare professionals and the public continues to grow. Health valuation studies, often in the form of discrete choice experiments, a choice based survey approach, proliferate as a result. A variety of methods of pre-choice process analysis have been developed to investigate how and why people make their decisions in such experiments and surveys. These techniques have been developed to investigate how people acquire and process information and make choices. These techniques offer the potential to test and improve theories of choice and/or associated empirical models. This paper provides an overview of such methods, with the focus on their use in stated choice-based healthcare studies. The methods reviewed are eye tracking, mouse tracing, brain imaging, deliberation time analysis and think aloud. For each method, we summarise the rationale, implementation, type of results generated and associated challenges, along with a discussion of possible future developments.
The nature and scope of the paper was developed by DR, CV and KP. CV led the search and drafting process for eye-tracking, mouse tracing and think-aloud sections; DR led this work for the deliberation time section; and KP and DR led this work for the brain imaging section. All authors provided critical review of the draft of the final manuscript.
Compliance with Ethical Standards
Caroline M. Vass and Katherine Payne were supported in the preparation and submission of this article by Mind the Risk international network collaboration funded by the Swedish Foundation for Humanities and Social Sciences. The views and opinions expressed are those of the authors, and not necessarily those of other Mind the Risk members or the Swedish Foundation for Humanities and Social Sciences.
Conflicts of interest
Dan Rigby, Caroline Vass and Katherine Payne have no conflicts of interest that are relevant to the content of this article.
- 4.Schlosser RW, Wendt O, Bhavnani S, et al. Use of information-seeking strategies for developing systematic reviews and engaging in evidence-based practice: the application of traditional and comprehensive Pearl Growing. A review. Int J Lang Commun Disord. 2006;41:567–82.PubMedCrossRefPubMedCentralGoogle Scholar
- 13.Holmqvist K, Nyström M, Andersson R, et al. Eye tracking: a comprehensive guide to methods and measures. Oxford: Oxford University Press; 2011.Google Scholar
- 14.Raney GE, Campbell SJ, Bovee JC. Using eye movements to evaluate the cognitive processes involved in text comprehension. J Vis Exp. 2014;83:1–7.Google Scholar
- 20.Chavez D, Palma M, Collart A. Eye tracking to model attribute attendance. San Antonio: Southern Agricultural Economics Association; 2016.Google Scholar
- 21.Chen Y, Caputo V, Nayga RM, et al. How visual attention affects choice outcomes: an eyetracking study. In: 3rd International Winter Conference on Brain–Computer Interface, BCI 2015; 2015.Google Scholar
- 22.Erdem S, McCarthy J. The effect of front-of-pack nutrition labelling formats on consumers’ food choices and decision-making: merging discrete choice experiment with an eye tracking experiment. Boston: Agricultural and Applied Economics Association; 2016.Google Scholar
- 24.Balcombe K, Fraser I, McSorley E. Visual attention and attribute attendance in multi-attribute choice experiments. J Appl Econom. 2014;30:1–27.Google Scholar
- 25.Grebitus C, Seitz C. Relationship between attention and choice. Naples: European Association of Agricultural Economists; 2014. p. 1–13.Google Scholar
- 28.Oviedo JL, Caparrós A. Information and visual attention in contingent valuation and choice modeling: field and eye-tracking experiments applied to reforestations in Spain. J For Econ. 2015;21:185–204.Google Scholar
- 32.Arieli A, Ben-Ami Y, Rubinstein A. Fairness motivations and procedures of choice between lotteries as revealed through eye movements. Foerder Institute for Economic Research Working Papers 275720; 2009.Google Scholar
- 33.Duchowski A. Eye tracking methodology: theory and practice. 2nd ed. New York: Springer; 2007.Google Scholar
- 36.MouseFlow https://mouseflow.com/. Accessed 17 Aug 2018.
- 37.MouseTracker http://www.mousetracker.org/.Accessed 11 Jun 2017.
- 38.Franco-Watkins A, Johnson J. Applying the decision moving window to risky choice: comparison of eye-tracking and mousetracing methods. Judgm Decis Mak. 2011;6:740–9.Google Scholar
- 39.Gray E. Time preference for future health events. PhD Thesis, HERU, University of Aberdeen; 2012.Google Scholar
- 41.Braeutigam S. Magnetoencephalography: fundamentals and established and emerging clinical applications in radiology. ISRN Radiol. 2013;12:529463.Google Scholar
- 43.Vecchiato G, Astolfi L, De Vico Fallani F, et al. On the use of EEG or MEG brain imaging tools in neuromarketing research. Comput Intell Neurosci. 2011;2011:643489.Google Scholar
- 45.Upright MRI http://www.uprightmri.co.uk/. Accessed 7 Jun 2017.
- 48.Khushaba RN, Kodagoda S, Dissanayake G, et al. A neuroscientific approach to choice modeling: electroencephalogram (EEG) and user preferences. In: Proceedings of the international joint conference on neural networks. 2012.Google Scholar
- 57.EMOTIV bioinformatics. San Francisco, USA: eMotiv. https://www.emotiv.com/.
- 58.Yale School of Medicine MRI Usage Charges. Yale University. http://mrrc.yale.edu/users/charges.aspx.
- 59.Ericsson K, Simon H. Protocol analysis: verbal reports as data (revised edition). Cambridge: MIT Press; 1993.Google Scholar
- 71.Conijn JM, van der Ark LA, Spinhoven P. Satisficing in mental health care patients: the effect of cognitive symptoms on self-report data quality. Assessment 2017;1–16.Google Scholar
- 74.Nvivo qualitative data analysis software, version 10. QSR International Pty Ltd.; 2014.Google Scholar
- 75.ATLAS.ti; Scientific Software Development GmbH, version 7; 2014.Google Scholar
- 79.Lauria DT, Whittington D, Kyeongae C, Turingan C, Abiad V. Household demand for improved sanitation services: a case study of Calamba, Philippines. In: Bateman IJ, Willis KG, editors. Valuing environmental preferences: theory and practice of the contingent valuation method in the US, EU, and developing countries. Oxford University Press; 2001. p. 540–81.Google Scholar
- 87.MacLachlan J, Myers JG. Using response latency to identify commercials that motivate. J Advert Res. 1983;23:51.Google Scholar
- 94.Xu P, Ehinger KA, Zhang Y, et al. TurkerGaze: crowdsourcing saliency with webcam based eye tracking. arXiv:1504.Google Scholar