Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data
Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data
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DOI：
10.1037/1082-989x.8.3.322
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发表时间：
2003-09-01
影响因子：
7
通讯作者：
Enders, CK
中科院分区：
文献类型：
--
作者：
Enders, CK
概述了一种两步法，用于获得具有项目级缺失数据的内部一致性可靠性估计。在第一步中，使用期望最大化(EM)算法获得协方差矩阵和均值向量。在第二步中，使用EM协方差矩阵作为输入，以通常的方式进行可靠性分析。蒙特卡罗模拟检验了6个变量(量表长度、回答类别、项目相关性、样本量、缺失数据和缺失数据技术)对3种不同结果的影响：估计偏差、平均误差和可信区间覆盖率。使用EM的两步法始终产生最准确的可靠性估计，并产生接近宣传的95%的覆盖率。概述了一种实施该过程的简单方法。
A 2-step approach for obtaining internal consistency reliability estimates with item-level missing data is outlined. In the 1st step, a covariance matrix and mean vector are obtained using the expectation maximization (EM) algorithm. In the 2nd step, reliability analyses are carried out in the usual fashion using the EM covariance matrix as input. A Monte Carlo simulation examined the impact of 6 variables (scale length, response categories, item correlations, sample size, missing data, and missing data technique) on 3 different outcomes: estimation bias, mean errors, and confidence interval coverage. The 2-step approach using EM consistently yielded the most accurate reliability estimates and produced coverage rates close to the advertised 95% rate. An easy method of implementing the procedure is outlined.