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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.

Suggested Citation

  • 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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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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