When using the mixture Rasch model, the model identification constraints are either to set the equal means for all classes in the assumed normal ability distributions (equal ability mean constraint in short), or to set the sum of item difficulties to be zero for each class. In real data analysis, however, both constraints are not always sufficient to establish a common scale across latent classes unless some items are specified as anchor items in the estimation. If these two conventional constraint approaches recover the class membership as good as the anchor item constraint approach, the conventional constraint approaches may be considered useful for the purpose of class membership classification. This study investigated agreement on class membership between one conventional constraint (the equal ability mean) and the anchor item constraint approaches. Results showed high agreement between these two constraint approaches, indicating that the conventional constraint of the equal mean ability approach may be used to recover the latent class membership although item profiles are not correctly estimated across latent classes.

中文翻译：

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混合Rasch模型中不同识别约束方法之间的潜在类成员资格分类的协议

使用混合Rasch模型时，模型识别约束条件是在假定的正态能力分布中为所有类别设置均等值（简称均等能力均值约束），或者将每个类别的项目难度总和设为零。但是，在实际数据分析中，除非在估计中将某些项目指定为锚项目，否则两个约束并不总是足以建立跨潜在类的通用标度。如果这两种常规约束方法恢复的类成员资格与锚点约束方法一样好，则常规约束方法可能被视为对类成员资格分类有用。这项研究调查了一种常规约束（均等能力均值）和锚项约束方法之间关于类成员关系的协议。结果表明，这两种约束方法之间的一致性很高，这表明，尽管未正确估计各个潜在类的项目配置文件，但均等能力方法的常规约束可用于恢复潜在类成员。