McNeish Daniel
a Department of Methodology and Statistics , Utrecht University , The Netherlands.
J Pers Assess. 2017 Nov-Dec;99(6):637-652. doi: 10.1080/00223891.2016.1252382. Epub 2016 Dec 8.
Exploratory factor analysis (EFA) is an extremely popular method for determining the underlying factor structure for a set of variables. Due to its exploratory nature, EFA is notorious for being conducted with small sample sizes, and recent reviews of psychological research have reported that between 40% and 60% of applied studies have 200 or fewer observations. Recent methodological studies have addressed small size requirements for EFA models; however, these models have only considered complete data, which are the exception rather than the rule in psychology. Furthermore, the extant literature on missing data techniques with small samples is scant, and nearly all existing studies focus on topics that are not of primary interest to EFA models. Therefore, this article presents a simulation to assess the performance of various missing data techniques for EFA models with both small samples and missing data. Results show that deletion methods do not extract the proper number of factors and estimate the factor loadings with severe bias, even when data are missing completely at random. Predictive mean matching is the best method overall when considering extracting the correct number of factors and estimating factor loadings without bias, although 2-stage estimation was a close second.
探索性因素分析（EFA）是一种极为常用的方法，用于确定一组变量的潜在因素结构。由于其探索性本质，EFA因常以小样本量进行而声名狼藉，近期对心理学研究的综述报告称，40%至60%的应用研究观测值为200个或更少。近期的方法学研究探讨了EFA模型的小样本量要求；然而，这些模型仅考虑了完整数据，而在心理学中完整数据是例外而非常规情况。此外，关于小样本缺失数据技术的现有文献匮乏，几乎所有现有研究关注的主题并非EFA模型的主要兴趣点。因此，本文进行了一项模拟，以评估各种缺失数据技术在小样本且存在缺失数据的EFA模型中的性能。结果表明，删除方法无法提取正确数量的因素，且在估计因素载荷时存在严重偏差，即使数据是完全随机缺失的。考虑到提取正确数量的因素且无偏差地估计因素载荷，预测均值匹配总体上是最佳方法，尽管两阶段估计紧随其后。
