Dinno Alexis
Center for Tobacco Control Research and Education University of California, San Francisco.
Multivariate Behav Res. 2009 May;44(3):362-388. doi: 10.1080/00273170902938969.
Horn's parallel analysis (PA) is the method of consensus in the literature on empirical methods for deciding how many components/factors to retain. Different authors have proposed various implementations of PA. Horn's seminal 1965 article, a 1996 article by Thompson and Daniel, and a 2004 article by Hayton, Allen, and Scarpello all make assertions about the requisite distributional forms of the random data generated for use in PA. Readily available software is used to test whether the results of PA are sensitive to several distributional prescriptions in the literature regarding the rank, normality, mean, variance, and range of simulated data on a portion of the National Comorbidity Survey Replication (Pennell et al., 2004) by varying the distributions in each PA. The results of PA were found not to vary by distributional assumption. The conclusion is that PA may be reliably performed with the computationally simplest distributional assumptions about the simulated data.
霍恩的平行分析（PA）是文献中关于确定保留多少个成分/因子的实证方法的共识方法。不同作者提出了PA的各种实现方式。霍恩1965年的开创性文章、汤普森和丹尼尔1996年的一篇文章以及海顿、艾伦和斯卡尔佩洛2004年的一篇文章都对用于PA的随机数据的必要分布形式做出了断言。通过改变每个PA中的分布，使用现成的软件来测试PA的结果是否对文献中关于国家共病调查复制（彭内尔等人，2004年）一部分模拟数据的秩、正态性、均值、方差和范围的几种分布规定敏感。结果发现PA的结果不会因分布假设而变化。结论是，PA可以在关于模拟数据的计算最简单的分布假设下可靠地进行。
