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.

中文翻译：

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检查与不完整数据集的度量相关性

讨论了在存在缺失数据的情况下估计和测试观察到的度量相关性的 2 阶段程序。该方法使用最大似然估计和错误发现率概念进行相关测试。该方法可用于具有缺失数据的初始探索性​​实证研究，在这些研究中，估计明显的变量相互关系指数和检验关于其总体值的假设是有意义的。通过包含辅助变量，该过程也适用于违反随机缺失的潜在假设。概述的方法用来自老化研究的数据进行了说明。