原始文献引用为

Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and PsychologicalMeasurement, 34 (1), 111-117.

如何确定是否可以做主成分分析。要看KMO的统计值，如果值>0.7，才表示适合做主成分分析！http://blog.sciencenet.cn/blog-74956-1058026.html  

完整pdf下载如下

Kaiser_1974.pdf

Hair et al_2010.pdf

Dziuban_1974.pdf

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相关论述①

In other words, KMO indicates the amount of variance  shared among  the items designed  to  measure  a  latent  variable   when compared  to that shared with the error.  Kaiser (1974)  recommends  accepting  values  greater  than  0.5 as acceptable.  More specifically,  values between 0.5 and 0.7  are  considered    mediocre,  values between 0.7 and 0.8 are considered good,  values between 0.8 and 0.9  are deemed    great  and  values  above  0.9  are  superb   (Hutcheson  and Sofroniou,  1999).  A v alue  more   than  0.7  is  the common threshold  for  confirmatory  analysis  (Hair et al., 2010).

http://qrclinic.blogspot.jp/2013/06/kmo-and-bartlets-test-in-factor-analysis.html

更多文献引用：

Dziuban, C. D., & Shirkey, E. C. (1974).When is a correlation matrix appropriate for factor analysis? PsychologicalBulletin, 81, 358-361.

Multivariate Data Analysis, 7/e. J. F. Hair Jr., W. C. Black, B. J. Babin, R. E. Anderson. 2010, Pearson Prentice Hall.

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相关论述②

The KMO statistic is a Measure of SamplingAdequacy, both overall and for each variable (Kaiser 1970; Cerny and Kaiser1977; Dziuban & Shirkey, 1974).

The SPSS program code sets KMO to .5 when the correlation matrix  is  an identity  matrix,  avoiding  the  division-by-0  problem.

http://www-01.ibm.com/support/docview.wss?uid=swg21479963

更多文献引用：

Cerny, C.A., & Kaiser, H.F. (1977). A studyof a measure of sampling adequacy for factor-analytic correlation matrices.Multivariate Behavioral Research, 12(1), 43-47.
Dziuban, C. D., & Shirkey, E. C. (1974). When is acorrelation matrix appropriate for factor analysis? Psychological Bulletin, 81,358-361.
Kaiser, H.F. (1970). A second generation Little Jiffy.Psychometrika, 35, 401-415.

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