The current widespread availability of software packages with estimation features for testing structural equation models with binary indicators makes it possible to investigate many hypotheses about differences in proportions over time that are typically only tested with conventional categorical data analyses for matched pairs or repeated measures, such as McNemar’s chi-square. The connection between these conventional tests and simple longitudinal structural equation models is described. The equivalence of several conventional analyses and structural equation models reveals some foundational concepts underlying common longitudinal modeling strategies and brings to light a number of possible modeling extensions that will allow investigators to pursue more complex research questions involving multiple repeated proportion contrasts, mixed between-subjects × within-subjects interactions, and comparisons of estimated membership proportions using latent class factors with multiple indicators. Several models are illustrated, and the implications for using structural equation models for comparing binary repeated measures or matched pairs are discussed.

中文翻译：

---

二元重复测量的结构模型：将现代纵向结构方程模型与匹配对的传统分类数据分析联系起来

具有估计功能的软件包目前普遍可用，用于测试具有二元指标的结构方程模型，这使得研究许多关于随时间变化的比例差异的假设成为可能，这些假设通常仅通过匹配对或重复测量的传统分类数据分析进行测试，例如McNemar 的卡方。描述了这些常规测试和简单的纵向结构方程模型之间的联系。几种传统分析和结构方程模型的等效性揭示了常见纵向建模策略背后的一些基本概念，并揭示了许多可能的建模扩展，这将使研究人员能够追求涉及多个重复比例对比的更复杂的研究问题，混合受试者间 × 受试者内相互作用，以及使用具有多个指标的潜在类别因子来比较估计的成员比例。说明了几个模型，并讨论了使用结构方程模型来比较二元重复测量或匹配对的含义。