Exploratory factor analysis for small samples
Exploratory factor analysis for small samples
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DOI：
10.3758/s13428-011-0077-9
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发表时间：
2011-09-01
影响因子：
5.4
通讯作者：
Lee, Soonmook
中科院分区：
文献类型：
--
作者：
Jung, Sunho;Lee, Soonmook
传统上，探索性因子分析有两种不同的方法：最大似然因子分析和主成分分析。第三种选择，称为正则化探索性因素分析，最近在心理测量学文献中引入。小样本是一个重要的问题，在因子分析文献中已经得到了相当多的讨论。然而，鲜为人知的是，这三种方法的探索性因素分析在小样本量的情况下的差异表现。模拟研究和实证分析表明，正则化的探索性因素分析，可以推荐在两个传统的方法，特别是当样本容量很小（低于50），样本协方差矩阵是近奇异的。
Traditionally, two distinct approaches have been employed for exploratory factor analysis: maximum likelihood factor analysis and principal component analysis. A third alternative, called regularized exploratory factor analysis, was introduced recently in the psychometric literature. Small sample size is an important issue that has received considerable discussion in the factor analysis literature. However, little is known about the differential performance of these three approaches to exploratory factor analysis in a small sample size scenario. A simulation study and an empirical example demonstrate that regularized exploratory factor analysis may be recommended over the two traditional approaches, particularly when sample sizes are small (below 50) and the sample covariance matrix is near singular.