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
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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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