A note on the use of missing auxiliary variables in full information maximum likelihood-based structural equation models
A note on the use of missing auxiliary variables in full information maximum likelihood-based structural equation models
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DOI：
10.1080/10705510802154307
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发表时间：
2008-07-01
影响因子：
6
通讯作者：
Enders, Craig K.
中科院分区：
文献类型：
--
作者：
Enders, Craig K.
最近的缺失数据研究主张采用“包容性分析策略”，将辅助变量纳入估计例程中，并且 Graham (2003) 概述了将辅助变量纳入结构方程分析的方法。在实践中，辅助变量经常存在缺失值，因此有理由询问包含此类变量是否会改善模型参数的估计。模拟结果表明，辅助变量的缺失数据比例和缺失数据机制对油偏影响不大。即使所有辅助变量不是随机缺失的，偏差也会被归入模型的辅助变量部分，并且不会传播到实质性利益模型中。研究结果表明，即使文件辅助变量有很大比例的缺失数据，包含辅助变量也是有益的。
Recent missing data studies have argued in favor of an "inclusive analytic strategy" that incorporates auxiliary variables into the estimation routine, and Graham (2003) outlined methods for incorporating auxiliary variables into Structural equation analyses. In practice, the auxiliary variables often have missing values, so it is reasonable to ask whether the inclusion Of Such variables will improve the estimation of model parameters. Simulation results indicated that the proportion of missing data and the missing data mechanism of the auxiliary variables had little impact oil bias. Even when all auxiliary variable was missing not at random, bias was relegated to file auxiliary variable portion of the model, and did not propagate into the model of Substantive interest. The study results Suggest that the inclusion of an auxiliary variable is beneficial, even if file auxiliary variable has a Substantial proportion of missing data.