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Investigating attribute non-attendance and its consequences in choice experiments with latent class models

机译:在潜在类模型的选择实验中调查属性缺勤及其后果

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摘要

A growing literature, mainly from transport and environment economics, has started to explore whether respondents violate some of the axioms about individuals' preferences in Discrete Choice Experiments (DCEs) and use simple strategies to make their choices. One of these strategies, termed attribute non-attendance (ANA), consists in ignoring one or more attributes. Using data from a DCE administered to healthcare providers in Ghana to evaluate their potential resistance to changes in clinical guidelines, this study illustrates how latent class models can be used in a step-wise approach to account for all possible ANA strategies used by respondents and explore the consequences of such behaviours. Results show that less than 3% of respondents considered all attributes when choosing between the two hypothetical scenarios proposed, with a majority looking at only one or two attributes. Accounting for ANA strategies improved the goodness-of-fit of the model and affected the magnitude of some of the coefficient and willingness-to-pay estimates. However, there was no difference in the predicted probabilities of the model taking into account ANA and the standard approach. Although the latter result is reassuring about the ability of DCEs to produce unbiased policy guidance, it should be confirmed by other studies.
机译:越来越多的文献(主要是运输和环境经济学的文献)已经开始探索受访者是否违反离散选择实验(DCE)中有关个人偏好的某些公理,并使用简单的策略进行选择。这些策略之一被称为属性无人参与(ANA),它忽略了一个或多个属性。使用从加纳的医疗保健提供者处获得的DCE数据评估他们对临床指南变化的潜在抵抗力,本研究说明了如何将潜在类别模型用于逐步方法,以解释受访者使用的所有可能的ANA策略并探索这种行为的后果。结果表明,只有不到3%的受访者在建议的两个假设情景之间进行选择时考虑了所有属性,而大多数只关注一个或两个属性。对ANA策略的会计处理改善了模型的拟合优度,并影响了一些系数的大小和支付意愿的估计。但是,考虑到ANA和标准方法,模型的预测概率没有差异。尽管后一结果使DCE产生公正的政策指导的能力令人放心,但其他研究应予以证实。

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