McArdle J J
Multivariate Behav Res. 1994 Oct 1;29(4):409-54. doi: 10.1207/s15327906mbr2904_5.
This article presents some benefits and limitations of structural equation models for multivariate experiments with incomplete data. Examples from studies of latent variable path models of cognitive performances illustrate analyses with four different kinds of incomplete data: (a) latent variables, (b) omitted variables, (c) randomly missing data, and (d) non- randomly missing data. Power based cost-benefit analyses for experimental design and planning are also presented. These incomplete data approaches are closely related to models used in classical experimental design, interbattery measurement analysis, longitudinal analyses, and behavioral genetic analyses. These structural equation methods for old experimental design problems indicate some new opportunities for future multivariate research.
本文介绍了结构方程模型在处理具有不完整数据的多变量实验时的一些优点和局限性。认知表现的潜在变量路径模型研究中的例子说明了对四种不同类型不完整数据的分析：（a）潜在变量，（b）遗漏变量，（c）随机缺失数据，以及（d）非随机缺失数据。还介绍了基于功效的成本效益分析在实验设计和规划中的应用。这些不完整数据方法与经典实验设计、电池间测量分析、纵向分析和行为遗传分析中使用的模型密切相关。这些针对旧实验设计问题的结构方程方法为未来的多变量研究指明了一些新的机会。
