Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that might also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches could result in a data analysis problem for which the missingness is ignorable. This article considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates and statistical inferences to assumptions about missing data, a strategy that could be easily implemented using SEM software.

中文翻译：

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数据不随机缺失时多线人模型的敏感性分析

缺失数据在依赖多个线人数据来评估变量之间的关系的研究中很常见，这些关系可区分的个体聚集在群体中。使用原始数据估计结构方程模型允许不完整的数据，因此即使只有一个组的成员提供数据，也可以保留所有组进行分析。统计推断基于数据完全随机丢失或随机丢失的假设。重要的是，假设数据是否缺失与缺失数据无关。当数据不是随机丢失时，将缺失或缺失数据的相关性合并到分析和多重插补中的饱和相关性模型也可能使用此类相关性提供优于 SEM 的标准实施的优势，因为这些方法可能导致数据分析缺失可忽略的问题。本文在家庭数据分析中考虑了这些方法，以评估参数估计和统计推断对缺失数据假设的敏感性，这种策略可以使用 SEM 软件轻松实现。