In this article, a modeling strategy is proposed that accounts for heterogeneity in nominal responses that is typically ignored when using common multinomial logit models. Heterogeneity can arise from unobserved variance heterogeneity, but it may also represent uncertainty in choosing from alternatives or, more generally, result from varying coefficients determined by effect modifiers. It is demonstrated that the bias in parameter estimation in multinomial logit models can be substantial if heterogeneity is present but ignored. The modeling strategy avoids biased estimates and allows researchers to investigate which variables determine uncertainty in choice behavior. Several applications demonstrate the usefulness of the model.

在本文中，提出了一种建模策略，该策略考虑了名义响应中的异质性，当使用通用多项式logit模型时，通常会忽略这种异质性。异质性可能是由未观察到的方差异质性引起的，但也可能代表着从替代方案中选择的不确定性，或更笼统地说，是由效果修饰符确定的系数变化所致。结果表明，如果存在异质性但忽略异质性，多项式对数模型中参数估计的偏差可能很大。该建模策略避免了有偏估计，并允许研究人员研究哪些变量确定选择行为的不确定性。几个应用程序证明了该模型的有用性。