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Conjoint Analysis and Discrete Choice Experiments for Quality Improvement

  • Autores: William Li, Christopher J. Nachtsheim, Ke Wang, Robert Reul, Mark Albrecht
  • Localización: Journal of quality technology: A quarterly journal of methods applications and related topics, ISSN 0022-4065, Vol. 45, Nº. 1, 2013, págs. 74-99
  • Idioma: inglés
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  • Resumen
    • Conjoint analysis and discrete choice experiments, which were developed in fields such as marketing and economics, are useful for understanding the voice of the customer to guide quality improvement efforts. Unfortunately, these methods have received relatively little attention in the quality area. This article provides some guidelines for the use of conjoint analysis and discrete choice experiments. The authors discuss what they are, why they are useful methodologies for quality improvement, and how a discrete choice experiment can be carried out. The methodology is demonstrated by a real case study in quality improvement. The authors then introduce a new class of designs for discrete choice experiments that are robust for a class of possible models. Several examples are provided in which an optimal design based on the main-effects only models is shown to have limited capability for estimation of two-factor interactions, whereas the proposed robust designs perform well in the presence of two-factor interactions. The article concludes with a summary of key points and directions for further research.


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