主成分分析与因子分析的区别联系。

相似文件 换一批

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

<P>请高手指点主成分分析与因子分析的区别与联系，谢谢</P>

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏3 回帖

关键词：主成分分析 因子分析 主成分 高手指点 主成分分析法 spss主成分分析 逐步回归分析 多元回归分析 因子分析法 应用时间序列分析

好问题，大伙积极发言

还没学到

看看我的老贴子，呵呵。。

不过，这个我最终还是没有完全解决。

只能说，二者都是对数据协方差矩阵的逼近！（《实用多元统计分析》中说的。)

[此贴子已经被作者于2006-4-17 23:09:18编辑过]

https://bbs.pinggu.org/thread-15405-1-1.html

这个也是我的问题，愿闻其详！

就我个人目前的理解来看，两者似乎是恰恰方向相反，主成分分析得到的因子是每个原变量的线性组合，而因子分析却是把各个原变量分解为公共因子的线性组合，方向相反。另外主成分分析提出８５％以上的贡献率的若干因子就可以了，而因子分析是对每个原变量都要解释．概括的说，前者是要从原变量众提炼出公共因子，而后者是已知公共因子的前提下对原变量的解释．不知道理解的对不对，请各位多指教．

Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) are both variable reduction techniques and sometimes mistaken as the same statistical methods. Howerver, there are distintive differences between PCA and EFA. Similarities and differences between PCA and EFA will be examined. Examples of PCA and EFA with PRINCOMP and FACTOR will be illustrated and discussed.

48857.pdf (149.19 KB)

2006-4-17 22:52:00 上传

[下载][推荐]Principla Component Analysis vs.Exploratory Factor Analysis

[此贴子已经被作者于2006-4-18 9:52:59编辑过]

楼上推荐的的确是一份好东东，应该能澄清我们的疑惑，呵呵。

刚下了，多谢。。[em07] [em10]

有空慢慢看

[em09]

Best Practices in Exploratory Factor Analysis: Four Recommendations

For Getting the Most From Your Analysis

Anna B. Costello and Jason W. Osborne North Carolina State University

PCA ( principal components analysis) is the default method of extraction in many popular statistical software packages, including SPSS and SAS, which likely contributes to its popularity. However, PCA is not a true method of factor analysis and there is disagreement among statistical theorists about when it should be used, if at all. Some argue for severely restricted use of components analysis in favor of a true factor analysis method ( Bentler & Kano, 1990; Floyd & Widaman, 1995; Ford, MacCallum & Tait, 1986; Gorsuch, 1990; Loehlin, 1990; MacCallum & Tucker, 1991; Mulaik, 1990; Snook & Gorsuch, 1989; Widaman, 1990, 1993). Others disagree, and point out either that there is almost no difference between principal components and factor analysis, or that PCA is preferable ( Arrindell & van der Ende, 1985; Guadagnoli and Velicer, 1988; Schoenmann, 1990; Steiger, 1990; Velicer & Jackson, 1990). We suggest that factor analysis is preferable to principal components analysis. Components analysis is only a data reduction method. It became common decades ago when computers were slow and expensive to use; it was a quicker, cheaper alternative to factor analysis ( Gorsuch, 1990). It is computed without regard to any underlying structure caused by latent variables; components are calculated using all of the variance of the manifest variables, and all of that variance appears in the solution ( Ford et al., 1986). However, researchers rarely collect and analyze data without an a priori idea about how the variables are related ( Floyd & Widaman, 1995). The aim of factor analysis is to reveal any latent variables that cause the underlying factor structure; only shared variance appears in the solution. Principal components analysis does not discriminate between shared and unique variance. When the factors are uncorrelated and communalities are moderate it can produce inflated values of variance accounted for by the components ( Gorsuch, 1997; McArdle, 1990). Since factor analysis only analyzes shared variance, factor analysis should yield the same solution ( all other things being equal) while also avoiding the inflation of estimates of variance accounted for.

[此贴子已经被作者于2006-4-18 9:54:31编辑过]