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Structural reliability of conjoint measurement in health care: an empirical investigation

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  • Stirling Bryan
  • David Parry
Abstract
This paper is concerned with the structural reliability of conjoint measurement when applied in a health care setting. The clinical context was the diagnosis and treatment of knee injuries. A conjoint measurement study was conducted which used the pairwise choice approach to preference elicitation. Each choice included two scenarios: a conventional treatment approach to management (arthroscopy) and an approach using magnetic resonance imaging. In order to test for structural reliability two separate conjoint measurement exercises were conducted: exercise A where scenarios were defined in terms of three attributes and exercise B where scenarios included all four attributes. The assessment of structural reliability involved a comparison of two random effects probit models, for exercises A and B. Data were collected on a total of 176 students of sports science. The results strongly indicate that the models for the two exercises are different, although the instability is limited to the constant term and a single model attribute (i.e. the avoidance of surgery). The finding of instability in the constant coefficient raises important questions about the appropriateness of labelling scenarios in conjoint measurement exercises.

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  • Stirling Bryan & David Parry, 2002. "Structural reliability of conjoint measurement in health care: an empirical investigation," Applied Economics, Taylor & Francis Journals, vol. 34(5), pages 561-567.
    • Handle: RePEc:taf:applec:v:34:y:2002:i:5:p:561-567
    DOI: 10.1080/00036840110103733
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    References listed on IDEAS

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    1. Johnson, Jane & Howard, Kirsten & Wilson, Andrew & Ward, Michael & Gilbert, Gwendolyn L. & Degeling, Chris, 2019. "Public preferences for One Health approaches to emerging infectious diseases: A discrete choice experiment," Social Science & Medicine, Elsevier, vol. 228(C), pages 164-171.
    2. Schwappach, David L.B. & Strasmann, Thomas J., 2006. ""Quick and dirty numbers"?: The reliability of a stated-preference technique for the measurement of preferences for resource allocation," Journal of Health Economics, Elsevier, vol. 25(3), pages 432-448, May.
    3. Kwarteng Michael Adu & Pilík Michal & Juřičková Eva, 2018. "Beyond cost saving. Other factor consideration in online purchases of used electronic goods: a conjoint analysis approach," Management & Marketing, Sciendo, vol. 13(3), pages 1051-1063, September.
    4. Takanori Ida & Shin Kinoshita & Masayuki Sato, 2008. "Conjoint analysis of demand for IP telephony: the case of Japan," Applied Economics, Taylor & Francis Journals, vol. 40(10), pages 1279-1287.
    5. Axel Mühlbacher & Uwe Junker & Christin Juhnke & Edgar Stemmler & Thomas Kohlmann & Friedhelm Leverkus & Matthias Nübling, 2015. "Chronic pain patients’ treatment preferences: a discrete-choice experiment," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(6), pages 613-628, July.
    6. Fredrik Carlsson & Peter Frykblom & Carl Lagerkvist, 2007. "Preferences with and without prices - does the price attribute affect behavior in stated preference surveys?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 38(2), pages 155-164, October.
    7. Trey A. Baird & Davene R. Wright & Maria T. Britto & Ellen A. Lipstein & Andrew T. Trout & Shireen E. Hayatghaibi, 2023. "Patient Preferences in Diagnostic Imaging: A Scoping Review," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 16(6), pages 579-591, November.
    8. Yeonbae Kim & Jeong-Dong Lee & Daeyoung Koh, 2005. "Effects of consumer preferences on the convergence of mobile telecommunications devices," Applied Economics, Taylor & Francis Journals, vol. 37(7), pages 817-826.

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