To distinguish between respondents that have attended to/ignored an attribute in discrete choice experiments (DCE), Hess and Hensher (HH) apply the coefficient of variation of the conditional distribution, setting a threshold of 2 as a conservative rule of thumb. This paper develops an analytical framework (piecewise regression analysis — PWRA) to refine the HH approach, offering a flexible method to identify attribute non-attendance (ANA) in highly context-dependent DCE. It is empirically tested on a dataset used to value agricultural public goods. The results suggest that the identification of non-attendance and goodness of fit of different random parameter logit models that accommodate ANA are better when the framework developed in this research is applied. When comparing welfare estimates from the HH and PWRA approach, significant differences are observed. Consequently, the flexibility of the PWRA notably contributes to revealing context-specific ANA patterns that can help to provide more accurate welfare measures and therefore policy recommendations.

为了区分在离散选择实验（DCE）中关注/忽略属性的受访者，Hess和Hensher（HH）应用条件分布的变异系数，将阈值2设置为保守的经验法则。本文开发了一个分析框架（分段回归分析— PWRA）来完善HH方法，提供了一种灵活的方法来识别高度依赖上下文的DCE中的属性非出勤（ANA）。在用于评估农业公共产品价值的数据集上进行了实证检验。结果表明，应用本研究开发的框架时，不同的适应ANA的随机参数logit模型的无人值守和拟合优度的识别效果更好。在比较HH和PWRA方法的福利估算时，观察到显着差异。因此，PWRA的灵活性显着有助于揭示特定于上下文的ANA模式，这可以帮助提供更准确的福利措施，从而提供政策建议。