A simple diagnostic measure of inattention bias in discrete choice models

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.