We introduce a restricted latent class exploratory model for longitudinal data with ordinal attributes and respondent-specific covariates. Responses follow a time inhomogeneous hidden Markov model where the probability of a respondent's latent state at the current time point is conditional on the respondent's latent state at the previous time point as well as the respondent's covariates at the current time point. We prove that the model is identifiable, state a Bayesian formulation, and demonstrate its efficacy in a variety of scenarios through two simulation studies. We apply the model to response data from a mathematics examination, comparing the results to a previously published confirmatory analysis, and also apply it to emotional state response data which was measured over a several-day period.

翻译：本文提出一种适用于具有序数属性及被试特异性协变量的纵向数据的受限潜在类别探索性模型。反应遵循时间非齐次隐马尔可夫模型，其中被试在当前时间点的潜在状态概率取决于其前一时间点的潜在状态以及当前时间点的协变量。我们证明了该模型的可识别性，给出了贝叶斯公式表述，并通过两项模拟研究展示了其在多种情境下的有效性。我们将该模型应用于数学考试的反应数据，将结果与先前发表的验证性分析进行对比，同时将其应用于持续数日测量的情绪状态反应数据。