有同样的问题,帮顶。我做出来的答案如下,是不是可以这样理解:按照spss的提示,这个分析的方法有些问题。
对于fa函数在R中计算结果不一致的原因,R本身在批注里面已经有一些解释了:
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Test cases comparing the output to SPSS suggest that the PA algorithm matches what SPSS calls uls, and that the wls solutions are equivalent in their fits. The wls and gls solutions have slightly larger eigen values, but slightly worse fits of the off diagonal residuals than do the minres or maximum likelihood solutions. Comparing the results to the examples in Harman (76), the PA solution with no iterations matches what Harman calls Principal Axes (as does SAS), while the iterated PA solution matches his minres solution. The minres solution found in psych tends to have slightly smaller off diagonal residuals (as it should) than does the iterated PA solution.
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SPSS will sometimes use a Kaiser normalization before rotating. This will lead to different solutions than reported here. To get the Kaiser normalized loadings, use kaiser.
========SPSS计算的结果=====================
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============*以下是R psych包的计算结果====================
fa(m1, nfactors = 3)$loadingsIn fa, too many factors requested for this number of
variables to use SMC for communality estimates, 1s are used insteadLoadings: MR1 MR2 MR3 v1 0.985 v2 0.953 v3 0.998v4 0.865v5 0.903 v6 1.023 MR1 MR2 MR3SS loadings 1.880 1.869 1.759Proportion Var 0.313 0.312 0.293Cumulative Var 0.313 0.625 0.918
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有一个问题:R语言psych包计算出来的既然与SPSS与SAS都不一样,那这个结果(R)的准确性如何?该如何理解?