The Impact of Missing Data on Sample Reliability Estimates: Implications for Reliability Reporting Practices
The Impact of Missing Data on Sample Reliability Estimates: Implications for Reliability Reporting Practices
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缺失数据对样本可靠性估计的影响：对可靠性报告实践的影响
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
Craig K. Enders
中科院分区：
文献类型：
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作者：
Craig K. Enders
概述了一种将最大似然（ML）估计纳入项目级缺失数据的可靠性分析的方法。首先使用期望最大化（EM）算法获得协方差矩阵的ML估计，然后使用标准公式计算系数alpha。一项模拟研究表明，emmethod产生(A)可靠性估计的偏差较小，(b)显著减少估计的跨样本波动，(c)产生更准确的置信区间。讨论了可靠性报告实践的含义，并使用启发式数据集演示了EM过程。
A method for incorporating maximum likelihood (ML) estimation into reliability analyses with item-level missing data is outlined. An ML estimate of the covariance matrix is first obtained using the expectation maximization (EM) algorithm, and coefficient alpha is subsequently computed using standard formulae. A simulation study demonstrated that the EMapproach yields (a) less bias in reliability estimates, (b) dramatically reduces cross-sample fluctuation of estimates, and (c) yields more accurate confidence intervals. Implications for reliability reporting practices are discussed, and the EM procedure is demonstrated using a heuristic data set.
影响因子：
7
作者：
L. Collins;J. Schafer;Chi-Ming Kam
通讯作者：
L. Collins;J. Schafer;Chi-Ming Kam
影响因子：
7.6
作者：
STEELE, CM;ARONSON, J
通讯作者：
ARONSON, J