ABSTRACT

A core challenge in modeling partial measurement invariance (MI) is choosing reference items as anchors for which MI indeed holds. Many approaches dealing with this issue have been proposed, each making a different assumption about MI and yielding a single set of anchor items. Here, we consider the case where i) partial MI modeling is used for estimating effects, e.g., a group mean difference, and ii) there is no straightforward theoretical reason to choose specific items as anchors. We argue that in this situation the uncertainty of anchor item choice should be considered and propose to use model averaging with a priori defined model weights. The approach allows not only to depict uncertainty in the anchor items choice but also allows to include prior knowledge and beliefs of the researcher. We derive the properties of the approach and illustrate its use with an example on the assessment of obsessive-compulsive disorder.

摘要

建模部分测量不变性 (MI) 的核心挑战是选择参考项目作为 MI 确实适用的锚。已经提出了许多处理这个问题的方法，每种方法都对 MI 做出不同的假设并产生一组锚项。在这里，我们考虑以下情况：i) 部分 MI 建模用于估计效果，例如，组均值差，以及 ii) 没有直接的理论理由选择特定项目作为锚点。我们认为，在这种情况下，应该考虑锚项目选择的不确定性，并建议使用具有先验定义的模型权重的模型平均。该方法不仅允许描述锚项目选择中的不确定性，还允许包括研究人员的先验知识和信念。