Researchers using discrete choice experiments (DCE) are often faced with the difficult decision of selecting which are the key attributes that must be included into their analysis. Previous literature on methods for attribute selection is not particularly well documented, frequently leaving researchers with a wide choice of attributes that could lead to complex choice tasks. Moreover, selecting attributes that might be ignored by the respondents might generate biased results, especially if attribute non-attendance is not taken into consideration. In this paper, we offer a framework for the selection of key attributes using eye-tracking software. Our main objective is to investigate if eye movements during the completion of a choice experiment can provide additional information to select the attributes in designing a DCE. Pretesting the DCE by an on-screen survey tool and eye-tracking, we implemented three multinomial logit models (MNL) to compare the stated preferences of the respondents, the self-reported statements on non-attendance and their visual attention to each attribute. The eye-tracking data revealed that all respondents looked at most attributes for most of the time. However, attention is different from attendance. Results show that eye-tracking can be a complementary method to self-reported statements by providing key information for reducing task complexity and potential attribute non-attendance in designing a DCE (one from seven attributes was dropped).

使用离散选择实验（DCE）的研究人员通常面临艰难的决定，即选择哪些是必须包含在他们的分析中的关键属性。以前关于属性选择方法的文献没有特别好的文献记载，经常使研究人员有广泛的属性选择，这可能导致复杂的选择任务。此外，选择可能被受访者忽略的属性可能会产生有偏差的结果，尤其是在不考虑属性缺勤的情况下。在本文中，我们提供了使用眼动追踪软件选择关键属性的框架。我们的主要目的是研究在选择实验完成期间眼球运动是否可以提供其他信息来选择设计DCE的属性。我们通过屏幕调查工具和眼动追踪对DCE进行了预测试，我们实施了三个多项式Lo​​git模型（MNL），以比较受访者的陈述偏好，自我报告的关于缺勤的陈述以及他们对每个属性的视觉关注。眼动数据显示，所有受访者大部分时间都在关注大多数属性。但是，注意力与出勤是不同的。结果表明，通过提供关键信息来减少任务复杂性和设计DCE时潜在的属性缺勤（从七个属性中删除一个），眼动追踪可以成为自我报告陈述的补充方法。自我报告的关于缺勤的陈述及其对每个属性的视觉关注。眼动数据显示，所有受访者大部分时间都在关注大多数属性。但是，注意力与出勤不同。结果表明，通过提供关键信息来减少任务复杂性和设计DCE时潜在的属性缺勤（从七个属性中删除一个），眼动追踪可以成为自我报告陈述的补充方法。自我报告的关于缺勤的陈述及其对每个属性的视觉关注。眼动数据显示，所有受访者大部分时间都在关注大多数属性。但是，注意力与出勤不同。结果表明，通过提供关键信息来减少任务复杂性和设计DCE时潜在的属性缺勤（从七个属性中删除一个），眼动追踪可以成为自我报告陈述的补充方法。