这是我在 IBM SPSS 中的代码：
FACTOR
/VARIABLES VAR00001 VAR00002 VAR00003 VAR00004 VAR00005 VAR00006
/MISSING LISTWISE
/ANALYSIS VAR00001 VAR00002 VAR00003 VAR00004 VAR00005 VAR00006
/PRINT UNIVARIATE INITIAL CORRELATION SIG DET KMO INV REPR AIC EXTRACTION ROTATION
/PLOT EIGEN ROTATION
/CRITERIA MINEIGEN(1) ITERATE(25)
/EXTRACTION PC
/CRITERIA ITERATE(25)
/ROTATION VARIMAX
/METHOD=CORRELATION.
这是 MATLAB R2015b 执行相同操作的代码：
[lambda,psi,T,stats,F]=factoran(DATA,2,'rotate','varimax');
旋转分量矩阵的 SPSS 输出：
Rotated Component Matrix
Component
1 2
VAR00001 .973 -.062
VAR00002 .911 -.134
VAR00003 .833 -.035
VAR00004 .972 -.102
VAR00005 -.236 .823
VAR00006 .062 .878
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 3 iterations.
MATLABlambda输出：
0.993085200854508 -0.0537771548307969
0.875990644597448 -0.147112975689921
0.748570753047806 -0.0343768914779775
0.987459815125692 -0.0988807726538385
-0.203059229288894 0.976610007465447
0.00719025397609984 0.475514010080256
为什么这些输出不同？我想在 MATLAB 中得到相同的结果。如您所知，SPSS 忽略小于 1 的特征值。我希望在 MATLAB 中具有相同的结构。我怎样才能做到这一点？
PS。
MATLABT输出：
0.622170579007477 -0.782881709211232
0.782881709211232 0.622170579007477
MATLABpsi输出：
0.0108898014620571
0.210998162961140
0.438460057014266
0.0151457063113246
0.00500000000002244
0.773834726466399
其他 SPSS 输出：
Component Matrix
Component
1 2
VAR00001 .964 .144
VAR00002 .919 .061
VAR00003 .821 .141
VAR00004 .971 .105
VAR00005 -.404 .755
VAR00006 -.124 .871
Extraction Method: Principal Component Analysis.
a 2 components extracted.
Component Transformation Matrix
Component 1 2
1 .977 -.211
2 .211 .977
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.