Measurement non-invariance arises when the psychometric properties of a scale differ across subgroups, undermining the validity of group comparisons. At the item level, this manifests as differential item functioning (DIF), where item responses differ across groups after controlling for the latent trait. This paper develops a framework for detecting DIF in ordinal scales without requiring known group labels or anchor items. We formulate a proportional-odds latent-class item response model in which individuals are assigned probabilistically to latent classes. DIF is captured through class-specific intercept and slope shifts, allowing both uniform and non-uniform DIF. Identification is achieved through an \(\ell_1\)-penalised marginal likelihood under a sparsity assumption, with estimation implemented using a tailored EM algorithm. Because class-specific slopes leave both the location and scale of each latent class unidentified, sparsity anchors the latent metric while selecting DIF effects. Simulation studies demonstrate accurate recovery of item parameters and both types of DIF. An empirical application to a personality test reveals latent subgroups with distinct response patterns and identifies items displaying potential class-specific measurement non-invariance. The framework provides a flexible approach for assessing measurement invariance in ordinal scales when comparison groups are unobserved or poorly defined.

翻译：测量非不变性是指量表在不同亚组间的心理测量学属性存在差异，从而削弱群体比较的有效性。在项目层面，这表现为差异项目功能（DIF），即在控制潜在特质后，不同群体对项目的响应存在差异。本文提出了一种无需已知群体标签或锚定项目即可检测有序量表中DIF的框架。我们构建了一个比例优势潜在类别项目反应模型，其中个体被概率性地分配到潜在类别中。DIF通过类别特定的截距和斜率变化来捕捉，允许同时存在均匀和非均匀DIF。在稀疏性假设下，通过\(\ell_1\)惩罚边际似然实现识别，并使用定制的EM算法进行估计。由于类别特定斜率会同时导致每个潜在类别的位置和尺度无法识别，稀疏性在锚定潜在度量的同时选择了DIF效应。模拟研究表明，该方法能够准确恢复项目参数以及两种类型的DIF。对一项人格测验的实证应用揭示了具有不同响应模式的潜在亚组，并识别出可能显示类别特异性测量非不变性的项目。该框架为在比较组未被观测或定义不清的情况下评估有序量表的测量不变性提供了一种灵活的方法。