We present an exploratory restricted latent class model where response data is for a single time point, polytomous, and differing across items, and where latent classes reflect a multi-attribute state where each attribute is ordinal. Our model extends previous work to allow for correlation of the attributes through a multivariate probit specification and to allow for respondent-specific covariates. We demonstrate that the model recovers parameters well in a variety of realistic scenarios, and apply the model to the analysis of a particular dataset designed to diagnose depression. The application demonstrates the utility of the model in identifying the latent structure of depression beyond single-factor approaches which have been used in the past.

翻译：本文提出一种探索性受限潜在类别模型，其响应数据为单时间点、多值且在不同项目间存在差异，潜在类别反映每个属性均为有序的多属性状态。该模型通过多元概率单位设定允许属性间存在相关性，并纳入受访者特异性协变量，从而扩展了先前研究。我们证明该模型在多种现实场景中均能良好地恢复参数，并将其应用于针对抑郁症诊断设计的特定数据集分析。该应用表明，相较于以往使用的单因子方法，本模型在识别抑郁症潜在结构方面具有显著优势。