In empirical science, many variables of interest are categorical. Like any model, models for categorical responses can be misspecified, leading to possibly large biases in estimation. One particularly troublesome source of misspecification is inattentive responding in questionnaires, which is well-known to jeopardize the validity of structural equation models (SEMs) and other survey-based analyses. I propose a general estimator that is designed to be robust to misspecification of models for categorical responses. Unlike hitherto approaches, the estimator makes no assumption whatsoever on the degree, magnitude, or type of misspecification. The proposed estimator generalizes maximum likelihood estimation, is strongly consistent, asymptotically Gaussian, has the same time complexity as maximum likelihood, and can be applied to any model for categorical responses. In addition, I develop a novel test that tests whether a given response can be fitted well by the assumed model, which allows one to trace back possible sources of misspecification. I verify the attractive theoretical properties of the proposed methodology in Monte Carlo experiments, and demonstrate its practical usefulness in an empirical application on a SEM of personality traits, where I find compelling evidence for the presence of inattentive responding whose adverse effects the proposed estimator can withstand, unlike maximum likelihood.

翻译：在实证科学中，许多感兴趣变量均为分类变量。与任何模型类似，分类响应模型可能存在设定错误，从而导致估计中产生较大偏差。一个尤为棘手的设定错误来源是问卷调查中的敷衍作答，这一现象众所周知会危及结构方程模型及其他基于调查的分析的有效性。本文提出一种通用估计量，旨在对分类响应模型的设定错误具有稳健性。与现有方法不同，该估计量对设定错误的程度、幅度或类型不作任何假设。所提估计量推广了极大似然估计，具有强相合性、渐近正态性，计算复杂度与极大似然估计相当，并适用于任何分类响应模型。此外，我开发了一种新颖检验，用于判断给定响应是否能够被假设模型良好拟合，从而追溯设定错误的可能来源。我在蒙特卡洛实验中验证了所提方法吸引人的理论性质，并在关于人格特质结构方程模型的实证应用中展示了其实用价值，其中我发现了令人信服的证据表明存在敷衍作答，而所提估计量能抵御其不利影响，这与极大似然估计不同。