A simple diagnostic measure of inattention bias in discrete choice models
提出一个取值0到1的简单度量，通过估计潜在类别logit模型中参数全为零的类别占比，来诊断离散选择模型中的注意力偏差，并用陷阱问题验证。
This note introduces a simple, easy-to-understand measure of inattention bias in discrete choice models. The metric, ranging from 0 to 1, can be compared across studies and samples. Specifically, a latent class logit model is estimated with all parameters in one class restricted to zero. The estimated share of observations falling in the class with null parameters (representing random choices) is the diagnostic measure of interest – the random response share. We validate the metric with an empirical study that identifies inattentive respondents via a trap question.