FACTOR: A computer program to fit the exploratory factor analysis model
FACTOR: A computer program to fit the exploratory factor analysis model
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DOI：
10.3758/bf03192753
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发表时间：
2006-02-01
影响因子：
5.4
通讯作者：
Ferrando, PJ
中科院分区：
文献类型：
--
作者：
Lorenzo-Seva, U;Ferrando, PJ
探索性因素分析（EFA）是心理学研究中应用最广泛的统计方法之一。它是一个经典的技术，但统计研究全民教育仍然是相当活跃的，各种新的发展和方法，近年来已经提出。然而，最受欢迎的统计软件包的作者似乎对纳入这些新进展并不感兴趣。我们提出的程序因子，这是设计作为一个通用的，用户友好的程序计算EFA。它采用了传统的程序和指数，并吸收了一些最新发展的好处。实施的两个传统程序是多元相关性和平行分析，后者被认为是确定要保留的因子或组分数量的最佳方法之一。在我们的程序中实现的最新发展的很好的例子是（1）最小秩因子分析，这是唯一允许计算每个因子解释的方差比例的因子方法，以及（2）最大旋转方法，这已被证明是最强大的旋转方法。在这些方法中，在某些商业程序中只有多区相关。可以从第一作者处免费获得软件副本、演示和简短手册。
Exploratory factor analysis (EFA) is one of the most widely used statistical procedures in psychological research. It is a classic technique, but statistical research into EFA is still quite active, and various new developments and methods have been presented in recent years. The authors of the most popular statistical packages, however, do not seem very interested in incorporating these new advances. We present the program FACTOR, which was designed as a general, user-friendly program for computing EFA. It implements traditional procedures and indices and incorporates the benefits of some more recent developments. Two of the traditional procedures implemented are polychoric correlations and parallel analysis, the latter of which is considered to be one of the best methods for determining the number of factors or components to be retained. Good examples of the most recent developments implemented in our program are (1) minimum rank factor analysis, which is the only factor method that allows one to compute the proportion of variance explained by each factor, and (2) the simplimax rotation method, which has proved to be the most powerful rotation method available. Of these methods, only polychoric correlations are available in some commercial programs. A copy of the software, a demo, and a short manual can be obtained free of charge from the first author.