Examining Measure Correlations with Incomplete Data Sets.
Examining Measure Correlations with Incomplete Data Sets.
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检查测量与不完整数据集的相关性。
DOI：
10.1080/10705511.2014.882696
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Lichtenberg,PeterA
中科院分区：
文献类型：
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作者：
Raykov,Tenko;Schneider,BrookeC;Marcoulides,GeorgeA;Lichtenberg,PeterA
讨论了在存在缺失数据的情况下估计和测试观测到的测量相关性的两阶段程序。该方法使用最大似然进行估计，并使用错误发现率概念进行相关性测试。该方法可用于缺失数据的初始探索型实证研究，其中有利于估计显变量相互关系指数并检验有关其总体值的假设。通过包含辅助变量，该程序也适用于违反随机缺失假设的情况。所概述的方法通过一项老龄化研究的数据进行了说明。
A 2-stage procedure for estimation and testing of observed measure correlations in the presence of missing data is discussed. The approach uses maximum likelihood for estimation and the false discovery rate concept for correlation testing. The method can be used in initial exploration-oriented empirical studies with missing data, where it is of interest to estimate manifest variable interrelationship indexes and test hypotheses about their population values. The procedure is applicable also with violations of the underlying missing at random assumption, via inclusion of auxiliary variables. The outlined approach is illustrated with data from an aging research study.