Hello everyone,
Further to my email below....I have found that this anomaly only occurs
in SPSS when the percentage of variance explained by the factors is very
similar e.g. in this example the percentage of the total variance
explained by one factor is 16.5% and the percentage of the total
variance explained by the other is 15.9%...hence SPSS has swapped the
factors around (and put them in the incorrect order). When the
percentage of the total variance explained by the factors is very
different then this anomaly does not occur, the factors are in the
correct order, and the results of SPSS correspond to the Minitab
solution.
Many thanks,
Kim.
-----Original Message-----
From: A UK-based worldwide e-mail broadcast system mailing list
[mailto:[log in to unmask]] On Behalf Of K F Pearce
Sent: 16 June 2005 12:28
To: [log in to unmask]
Subject: SPSS and Mintab comparisons
Hello everyone,
I was comparing the results of a Factor Analysis in Minitab and SPSS
[v11 & v12] (covariance based with Varimax rotation) and I noticed that
SPSS printed Factors 2 and 3 in the opposite order. Has anyone noticed
this too? I think it must be a bug in SPSS as the factors should be
arranged in decreasing order of the variance explained, and as we can
see from the output below this is not the case for the SPSS solution.
The Minitab solution does, however, appear to be correct.
Many thanks,
Kim
Minitab Output
Rotated Factor Loadings and Communalities Varimax Rotation
Variable Factor1 Factor2 Factor3 Factor4 Communality
var1 1.103 0.520 -0.287 -0.085 1.577
var2 0.160 0.168 -0.894 0.061 0.857
var3 0.001 0.082 -0.044 -1.001 1.010
var4 0.916 0.835 -0.296 -0.046 1.625
var5 1.027 0.497 -0.296 -0.043 1.392
var6 1.249 0.307 -0.278 -0.112 1.743
var7 -0.193 -0.371 0.297 0.284 0.344
var8 1.059 0.088 -0.396 -0.043 1.288
var9 0.438 0.868 -0.357 -0.109 1.085
var10 0.274 0.249 -0.640 -0.099 0.556
var11 0.393 0.153 -0.566 -0.337 0.611
Variance 6.2750 2.3286 2.2385 1.2470 12.0890
% Var 0.445 0.165 0.159 0.088 0.857
SPSS OUTPUT
Rotated Component Matrix
Raw
Component
1 2 3 4
VAR00001 1.103 .287 .520 .085
VAR00002 .160 .894 .168 -.061
VAR00003 .000 .044 .082 1.001
VAR00004 .916 .296 .835 .046
VAR00005 1.027 .296 .497 .043
VAR00006 1.249 .278 .307 .112
VAR00007 -.193 -.297 -.371 -.284
VAR00009 1.059 .396 .088 .043
VAR00010 .438 .357 .868 .109
VAR00011 .274 .640 .249 .099
VAR00012 .393 .566 .153 .337
Extraction Method: Principal Component Analysis. Rotation Method:
Varimax with Kaiser Normalization.
a Rotation converged in 5 iterations.
Total Variance Explained
Component % of Var Cumulative %
1 44.486 44.486
2 15.870 60.356
3 16.511 76.867
4 8.840 85.707
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