Erdem Seda, Campbell Danny, Hole Arne Risa
Economics Division, Stirling Management School, University of Stirling, Stirling, UK.
Department of Economics, University of Sheffield, Sheffield, UK.
Health Econ. 2015 Jul;24(7):773-89. doi: 10.1002/hec.3059. Epub 2014 May 5.
An extensive literature has established that it is common for respondents to ignore attributes of the alternatives within choice experiments. In most of the studies on attribute non-attendance, it is assumed that respondents consciously (or unconsciously) ignore one or more attributes of the alternatives, regardless of their levels. In this paper, we present a new line of enquiry and approach for modelling non-attendance in the context of investigating preferences for health service innovations. This approach recognises that non-attendance may not just be associated with attributes but may also apply to the attribute's levels. Our results show that respondents process each level of an attribute differently: while attending to the attribute, they ignore a subset of the attribute's levels. In such cases, the usual approach of assuming that respondents either attend to the attribute or not, irrespective of its levels, is erroneous and could lead to misguided policy recommendations. Our results indicate that allowing for attribute-level non-attendance leads to substantial improvements in the model fit and has an impact on estimated marginal willingness to pay and choice predictions.
大量文献表明，在选择实验中，受访者忽略备选方案属性的情况很常见。在大多数关于属性不关注的研究中，假设受访者有意识地（或无意识地）忽略备选方案的一个或多个属性，而不管其水平如何。在本文中，我们提出了一种新的研究思路和方法，用于在调查对卫生服务创新的偏好的背景下对不关注情况进行建模。这种方法认识到，不关注可能不仅与属性相关，还可能适用于属性的水平。我们的结果表明，受访者对属性的每个水平的处理方式不同：在关注该属性时，他们会忽略该属性水平的一个子集。在这种情况下，通常假设受访者要么关注该属性，要么不关注，而不管其水平如何的方法是错误的，可能会导致误导性的政策建议。我们的结果表明，考虑属性水平的不关注会显著改善模型拟合，并对估计的边际支付意愿和选择预测产生影响。
