Huang Sijia, Jeon Minjeong
School of Education, Indiana University Bloomington, Bloomington, IN, United States.
School of Education and Information Studies, University of California, Los Angeles, Los Angeles, CA, United States.
Front Psychol. 2022 Oct 24;13:976964. doi: 10.3389/fpsyg.2022.976964. eCollection 2022.
Cross-classified random effects models (CCREMs) have been developed for appropriately analyzing data with a cross-classified structure. Despite its flexibility and the prevalence of cross-classified data in social and behavioral research, CCREMs have been under-utilized in applied research. In this article, we present CCREMs as a general and flexible modeling framework, and present a wide range of existing models designed for different purposes as special instances of CCREMs. We also introduce several less well-known applications of CCREMs. The flexibility of CCREMs allows these models to be easily extended to address substantive questions. We use the free R package to illustrate the estimation of these models, and show how the general language of the CCREM framework can be translated into specific modeling contexts.
交叉分类随机效应模型（CCREMs）已被开发用于对具有交叉分类结构的数据进行适当分析。尽管其具有灵活性且交叉分类数据在社会和行为研究中普遍存在，但CCREMs在应用研究中一直未得到充分利用。在本文中，我们将CCREMs作为一个通用且灵活的建模框架进行介绍，并将为不同目的设计的各种现有模型作为CCREMs的特殊实例进行展示。我们还介绍了CCREMs的一些不太知名的应用。CCREMs的灵活性使这些模型能够轻松扩展以解决实质性问题。我们使用免费的R包来说明这些模型的估计，并展示CCREM框架的通用语言如何转化为特定的建模环境。
