9
$\begingroup$

For my PhD thesis I have to do a Principal Component Analysis (PCA). I didn't find it too difficult in Stata and was happy interpreting the results (I know there is a difference between factor and principal component analysis). However, I discussed it with a colleague who uses SPSS, so I imported my data (from Excel) into SPSS too, and performed a PCA in there as well.

Shockingly for me, the results differ enormously from my Stata results (after rotation). Not even close to it.

How can that be? (See Stata PCA and SPSS PCA codes and results below).

Even stranger to me: When I did a factor [varnames], pcf (principal-component factor) in Stata I received (almost) the same results as for PCA in SPSS (see Stata principal-component factor below).

What is principal component factors? A mixture of PCA and factor analysis?

I am confused. If people report in journals having done a PCA: should I then ask, with SPSS or Stata? Can anyone explain it to me?

Stata:

pca bewert_sfu_a bewert_sfu_b bewert_sfu_c bewert_sfu_d bewert_sfu_e bewert_sfu_f bewert_sfu_g bewert_sfu_h bewert_sfu_i bewert_sfu_j bewert_sfu_k bewert_sfu_l, mineigen(1)

Principal components/correlation
Number of obs = 158 Number of comp. = 3 Trace = 12 Rotation: (unrotated = principal) Rho = 0.5382

--------------------------------------------------------------------------
   Component |   Eigenvalue   Difference         Proportion   Cumulative
-------------+------------------------------------------------------------
       Comp1 |       3.8723      2.46548             0.3227       0.3227
       Comp2 |      1.40682      .227718             0.1172       0.4399
       Comp3 |       1.1791      .206742             0.0983       0.5382
       Comp4 |      .972359      .169164             0.0810       0.6192
       Comp5 |      .803195      .050871             0.0669       0.6861
       Comp6 |      .752324     .0953662             0.0627       0.7488
       Comp7 |      .656957     .0137592             0.0547       0.8036
       Comp8 |      .643198      .135894             0.0536       0.8572
       Comp9 |      .507304     .0435925             0.0423       0.8995
      Comp10 |      .463711     .0749052             0.0386       0.9381
      Comp11 |      .388806     .0348752             0.0324       0.9705
      Comp12 |      .353931            .             0.0295       1.0000
--------------------------------------------------------------------------

Principal components (eigenvectors) 

----------------------------------------------------------
    Variable |    Comp1     Comp2     Comp3 | Unexplained 
-------------+------------------------------+-------------
bewert_sfu_a |   0.2700    0.3901   -0.1477 |       .4779 
bewert_sfu_b |   0.3298    0.2303   -0.4027 |       .3129 
bewert_sfu_c |  -0.3046    0.3149    0.1773 |       .4642 
bewert_sfu_d |   0.3489    0.1910    0.0700 |       .4715 
bewert_sfu_e |   0.3342    0.2067    0.2720 |       .4202 
bewert_sfu_f |  -0.2001    0.4561   -0.1587 |       .5227 
bewert_sfu_g |   0.3057    0.3128    0.1531 |       .4728 
bewert_sfu_h |  -0.3611    0.2180    0.2913 |        .328 
bewert_sfu_i |   0.2352   -0.2211    0.3662 |       .5588 
bewert_sfu_j |  -0.1556    0.3894    0.4578 |       .4457 
bewert_sfu_k |   0.3239    0.0525    0.0754 |       .5832 
bewert_sfu_l |   0.2091   -0.2445    0.4720 |       .4839 
----------------------------------------------------------

rotate, varimax kaiser

Principal components/correlation Number of obs = 158 Number of comp. = 3 Trace = 12 Rotation: orthogonal varimax (Kaiser on) Rho = 0.5382

--------------------------------------------------------------------------
   Component |     Variance   Difference         Proportion   Cumulative
-------------+------------------------------------------------------------
       Comp1 |      2.95242      .867357             0.2460       0.2460
       Comp2 |      2.08506       .66433             0.1738       0.4198
       Comp3 |      1.42073            .             0.1184       0.5382
--------------------------------------------------------------------------

Rotated components 

----------------------------------------------------------
    Variable |    Comp1     Comp2     Comp3 | Unexplained 
-------------+------------------------------+-------------
bewert_sfu_a |   0.4076   -0.0266   -0.2829 |       .4779 
bewert_sfu_b |   0.3116   -0.3063   -0.3648 |       .3129 
bewert_sfu_c |  -0.0255    0.4536   -0.1302 |       .4642 
bewert_sfu_d |   0.4007   -0.0456    0.0218 |       .4715 
bewert_sfu_e |   0.4392    0.0965    0.1618 |       .4202 
bewert_sfu_f |   0.0698    0.2650   -0.4451 |       .5227 
bewert_sfu_g |   0.4531    0.0973    0.0005 |       .4728 
bewert_sfu_h |  -0.1026    0.5023    0.0011 |        .328 
bewert_sfu_i |   0.1350   -0.0261    0.4684 |       .5588 
bewert_sfu_j |   0.1927    0.5856    0.0731 |       .4457 
bewert_sfu_k |   0.3026   -0.1048    0.1037 |       .5832 
bewert_sfu_l |   0.1224    0.0410    0.5564 |       .4839 
----------------------------------------------------------

Component rotation matrix

--------------------------------------------
             |    Comp1     Comp2     Comp3 
-------------+------------------------------
       Comp1 |   0.7942   -0.5573    0.2422 
       Comp2 |   0.5724    0.5523   -0.6061 
       Comp3 |   0.2040    0.6200    0.7576 
--------------------------------------------

SPSS Code:

FACTOR
/VARIABLES bewert_sfu_a bewert_sfu_b bewert_sfu_c bewert_sfu_d  bewert_sfu_e bewert_sfu_f bewert_sfu_g bewert_sfu_h bewert_sfu_i bewert_sfu_j bewert_sfu_k bewert_sfu_l
/MISSING LISTWISE
/ANALYSIS bewert_sfu_a bewert_sfu_b bewert_sfu_c bewert_sfu_d bewert_sfu_e bewert_sfu_f bewert_sfu_g bewert_sfu_h bewert_sfu_i bewert_sfu_j bewert_sfu_k bewert_sfu_l
/PRINT EXTRACTION ROTATION
/FORMAT BLANK(.40)
/CRITERIA MINEIGEN(1) ITERATE(50)
/EXTRACTION PC
/CRITERIA ITERATE(50)
/ROTATION VARIMAX
/METHOD=CORRELATION.

Descriptive Statistics

                Mean    Std. Deviation  Analysis N
bewert_sfu_a    3.79              .452  158
bewert_sfu_b    3.68              .506  158
bewert_sfu_c    1.61              .827  158
bewert_sfu_d    3.32              .619  158
bewert_sfu_e    3.03              .643  158
bewert_sfu_f    2.61              .812  158
bewert_sfu_g    3.32              .621  158
bewert_sfu_h    1.53              .796  158
bewert_sfu_i    2.10              .838  158
bewert_sfu_j    2.53              .819  158
bewert_sfu_k    3.29              .784  158
bewert_sfu_l    2.78              .842  158`

Component Matrix a

                       Component        
                   1       2       3
bewert_sfu_a    .531    .463    
bewert_sfu_b    .649           -.437
bewert_sfu_c   -.599        
bewert_sfu_d    .687        
bewert_sfu_e    .658        
bewert_sfu_f            .541    
bewert_sfu_g    .602        
bewert_sfu_h   -.711        
bewert_sfu_i    .463        
bewert_sfu_j            .462    .497
bewert_sfu_k    .637        
bewert_sfu_l    .412            .513

Extraction Method: Principal Component Analysis.
a 3 components extracted.

Communalities

                Extraction
bewert_sfu_a    .522
bewert_sfu_b    .687
bewert_sfu_c    .536
bewert_sfu_d    .529
bewert_sfu_e    .580
bewert_sfu_f    .477
bewert_sfu_g    .527
bewert_sfu_h    .672
bewert_sfu_i    .441
bewert_sfu_j    .554
bewert_sfu_k    .417
bewert_sfu_l    .516
Extraction Method: Principal Component Analysis.

Rotated Component Matrix a

                      Component     
                   1      2      3
bewert_sfu_a    .705        
bewert_sfu_b    .673  -.448 
bewert_sfu_c           .627 
bewert_sfu_d    .671        
bewert_sfu_e    .661        
bewert_sfu_f                 -.576
bewert_sfu_g    .699        
bewert_sfu_h           .698 
bewert_sfu_i                  .630
bewert_sfu_j           .742 
bewert_sfu_k    .528        
bewert_sfu_l                  .707

Extraction Method: Principal Component Analysis. 
Rotation Method: Varimax with Kaiser Normalization.         
a Rotation converged in 5 iterations.

Component Transformation Matrix

Component      1        2      3
1           .765    -.476   .434
2           .644     .567  -.513
3          -.001     .672   .741
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.

Stata factor analysis/correlation
Number of obs = 158 Method: principal-component factors
Retained factors = 3 Rotation: (unrotated)
Number of params = 33

--------------------------------------------------------------------------
     Factor  |   Eigenvalue   Difference        Proportion   Cumulative
-------------+------------------------------------------------------------
    Factor1  |      3.87230      2.46548            0.3227       0.3227
    Factor2  |      1.40682      0.22772            0.1172       0.4399
    Factor3  |      1.17910      0.20674            0.0983       0.5382
    Factor4  |      0.97236      0.16916            0.0810       0.6192
    Factor5  |      0.80319      0.05087            0.0669       0.6861
    Factor6  |      0.75232      0.09537            0.0627       0.7488
    Factor7  |      0.65696      0.01376            0.0547       0.8036
    Factor8  |      0.64320      0.13589            0.0536       0.8572
    Factor9  |      0.50730      0.04359            0.0423       0.8995
   Factor10  |      0.46371      0.07491            0.0386       0.9381
   Factor11  |      0.38881      0.03488            0.0324       0.9705
   Factor12  |      0.35393            .            0.0295       1.0000
--------------------------------------------------------------------------
LR test: independent vs. saturated:  chi2(66) =  453.95 Prob>chi2 = 0.0

Factor loadings (pattern matrix) and unique variances

-----------------------------------------------------------
    Variable |  Factor1   Factor2   Factor3 |   Uniqueness 
-------------+------------------------------+--------------
bewert_sfu_a |   0.5314    0.4627   -0.1603 |      0.4779  
bewert_sfu_b |   0.6490    0.2732   -0.4373 |      0.3129  
bewert_sfu_c |  -0.5994    0.3735    0.1926 |      0.4642  
bewert_sfu_d |   0.6866    0.2265    0.0760 |      0.4715  
bewert_sfu_e |   0.6576    0.2451    0.2954 |      0.4202  
bewert_sfu_f |  -0.3938    0.5409   -0.1723 |      0.5227  
bewert_sfu_g |   0.6015    0.3710    0.1663 |      0.4728  
bewert_sfu_h |  -0.7107    0.2586    0.3163 |      0.3280  
bewert_sfu_i |   0.4629   -0.2622    0.3977 |      0.5588  
bewert_sfu_j |  -0.3062    0.4619    0.4971 |      0.4457  
bewert_sfu_k |   0.6373    0.0623    0.0818 |      0.5832  
bewert_sfu_l |   0.4116   -0.2900    0.5125 |      0.4839

rotate, varimax kaiser blanks(.4)

Factor analysis/correlation
Number of obs = 158 Method: principal-component factors
Retained factors = 3 Rotation: orthogonal varimax (Kaiser on)
Number of params = 33

--------------------------------------------------------------------------
     Factor  |     Variance   Difference        Proportion   Cumulative
-------------+------------------------------------------------------------
    Factor1  |      2.84986      0.98705            0.2375       0.2375
    Factor2  |      1.86281      0.11727            0.1552       0.3927
    Factor3  |      1.74554            .            0.1455       0.5382
--------------------------------------------------------------------------
LR test: independent vs. saturated:  chi2(66) =  453.95 Prob>chi2 = 0.0000

Rotated factor loadings (pattern matrix) and unique variances

-----------------------------------------------------------
    Variable |  Factor1   Factor2   Factor3 |   Uniqueness 
-------------+------------------------------+--------------
bewert_sfu_a |   0.7047   -0.0983   -0.1258 |      0.4779  
bewert_sfu_b |   0.6732   -0.4479   -0.1827 |      0.3129  
bewert_sfu_c |  -0.2184    0.6266   -0.3090 |      0.4642  
bewert_sfu_d |   0.6710   -0.1473    0.2377 |      0.4715  
bewert_sfu_e |   0.6605    0.0245    0.3781 |      0.4202  
bewert_sfu_f |   0.0474    0.3785   -0.5761 |      0.5227  
bewert_sfu_g |   0.6989    0.0358    0.1935 |      0.4728  
bewert_sfu_h |  -0.3776    0.6976   -0.2067 |      0.3280  
bewert_sfu_i |   0.1847   -0.1019    0.6298 |      0.5588  
bewert_sfu_j |   0.0624    0.7419   -0.0018 |      0.4457  
bewert_sfu_k |   0.5276   -0.2131    0.3050 |      0.5832  
bewert_sfu_l |   0.1273   -0.0160    0.7069 |      0.4839  
-----------------------------------------------------------

Factor rotation matrix

-----------------------------------------
             | Factor1  Factor2  Factor3 
-------------+---------------------------
     Factor1 |  0.7650  -0.4761   0.4336 
     Factor2 |  0.6440   0.5672  -0.5134 
     Factor3 | -0.0016   0.6720   0.7406 
-----------------------------------------
Improve this question
$\endgroup$
14
  • 3
    $\begingroup$ Can you show code and output. It's hard to guess without knowing exactly what you did. $\endgroup$
    – Jeremy Miles
    Commented May 28, 2015 at 0:47
  • $\begingroup$ Spelling is Stata not STATA. The company used the spelling STATA briefly in 1985, but not since. Corrected throughout. (factor is a command in Stata, not a function, although that's not material to the question.) $\endgroup$
    – Nick Cox
    Commented May 28, 2015 at 8:34
  • $\begingroup$ Cross-posted here statalist.org/forums/forum/general-stata-discussion/general/… Telling us about cross-posting is helpful in any forum. $\endgroup$
    – Nick Cox
    Commented May 28, 2015 at 11:07
  • 2
    $\begingroup$ I don't see any appreciable differences between the PCA results. For example, the first three eigenvalues for SPSS are 3.782, 1.407, 1.179 compared to the Stata calculations of 3.8723, 1.40682, 1.1791: in perfect agreement. I'm confident they correspond to the same eigenvectors in both cases. Please tell us what your basis is for concluding that the two programs give different results for PCA. (I note that you have asked for additional followup calculations in the form of factor analysis which the question strongly distinguishes from PCA and therefore should be ignored.) $\endgroup$
    – whuber ♦
    Commented May 28, 2015 at 13:15
  • 2
    $\begingroup$ Dear whuber, thanks for looking into it. The results after the rotation are very different, i.e. not the same amount of variables loading on the components and the factor loadings are different, too. (As you can see, I did use the same rotation method). Do I misunderstand something completely? $\endgroup$
    – hanne
    Commented May 28, 2015 at 13:23

4 Answers 4

Reset to default
7
$\begingroup$

You are correct. Stata is weird about this. Stata gives different results from SAS, R and SPSS, and it is difficult (in my opinion) to understand why without delving quite deep into the world of factor analysis and PCA.

Here's how you know that something weird is happening. The sum of the squared loadings for a component are equal to the eigenvalue for that component.

Pre-and post-rotation, the eigenvalues change, but the total eigenvalues don't change. Add up the sum of the squared loadings from your output (this is why I asked you to remove the blanks in my comment). With Stata's default, the sum of squared loadings will sum to 1.00 (within rounding error). With SPSS (and R, and SAS, and every other factor analysis program I've looked at) they will sum to the eigenvalue for that factor. (Post rotation eigenvalues change, but the sum of eigenvalues stays the same). The sum of squared loadings in SPSS is equal to the sum of the eigenvalues (i.e. 3.8723 + 1.40682), both pre- and post-rotation.

In Stata, the sum of the squared loadings for each factor is equal to 1.00, and so Stata has rescaled the loadings.

The only mention of this (that I have found) in the Stata documentation is in the estat loadings section of the help, where it says:

cnorm(unit | eigen | inveigen), an option used with estat loadings, selects the normalization of the eigenvectors, the columns of the principal-component loading matrix. The following normalizations are available

However, this appears to apply only to the unrotated component matrix, not the component rotated matrix. I can't get the unnormalized rotated matrix after PCA.

The people at Stata seem to know what they are doing, and usually have a good reason for doing things the way that they do. This one is beyond me though.

(For future reference, it would have made my life easier if you'd used a dataset that I could access, and if you'd included all output, without blanks).

Edit: My usual go-to site for information about how to get the same results for different programs is the UCLA IDRE. They don't cover PCA in Stata: http://www.ats.ucla.edu/stat/AnnotatedOutput/ I have to wonder if that's because they couldn't get the same result. :)

Improve this answer
$\endgroup$
8
  • 2
    $\begingroup$ Can this Stata weirdness be of the same sort as raised in another recent question? There clearly Stata rotated the eigenvector matrix (that is, the normalized loadings with SS=1), whereas most programs rotate loading matrix. $\endgroup$
    – ttnphns
    Commented May 28, 2015 at 18:00
  • 1
    $\begingroup$ In the documentation it is stated Remark: Literature and software that treat principal components in combination with factor analysis tend to isplay principal components normed to the associated eigenvalues rather than to 1. This normalization is available in the postestimation command estat loadings; see [MV] pca postestimation. So yes, both you and I were right in our suspicions. $\endgroup$
    – ttnphns
    Commented May 28, 2015 at 18:11
  • 1
    $\begingroup$ Thank you guys for helping me with my problem, let me sum up what I understood: The initial calculation (before rotation) of a PCA in Stata and SPSS is the same, i.e. same Eigenvalues, number of components (given you select the same options in Stata and SPSS (mineigen(1) etc.) The same is true for Statas command: `factor [varlist], pcf'. $\endgroup$
    – hanne
    Commented May 29, 2015 at 10:29
  • 2
    $\begingroup$ Yes, I believe that you have it. $\endgroup$
    – Jeremy Miles
    Commented May 29, 2015 at 17:11
  • 2
    $\begingroup$ william lisowski at statalist puts it in a more professional way than i did: statalist.org/forums/forum/general-stata-discussion/general/… but in the end coming to the same conclusion :) $\endgroup$
    – hanne
    Commented May 29, 2015 at 23:18
3
$\begingroup$

The differences between the Stata PCA methods and the conventional methods used in R or SPSS are:

1. Scaling eigenvectors/components

Stata rotates eigenvectors. Whereas, R or SPSS PCA-rotation methods normally rotates after scaling eigenvectors by the sqrt of the eigenvalues to produce the component loadings more typical in factor analysis.

2. Convergence stopping criteria (tolerance)

The other difference is that Stata, rotations convergence stopping criteria (tolerance) is 1e-6 for varimax rotation (according to Stat's rotatemat function manual), while in R the default is 1e-5, and in SPSS the default is 1e-14, if I am not mistaken. After addressing these differences, I was able to get identical loadings and an identical rotation matrix in R.

R and SPSS varimax rotation function by default perform Kaiser normalization.

However, varimax rotations on eigenvectors directly seem to be unconventional. See the discussion titled Is PCA followed by a rotation (such as varimax) still PCA?.

Addendum

Each PC is orthogonal (uncorrelated). However, rotating PCs has neither orthogonal loadings nor uncorrelated components once the rotation is done. This is what has happened in the Stata codes. To obtain uncorrelated components a special scaling is needed, such as dividing the components by the square root of the corresponding eigenvalue (eigenvalues are the variances of the corresponding PCs), which is the default scaling method in R and SPSS.

References:

Improve this answer
$\endgroup$
2
  • $\begingroup$ Thank you for this exhaustive answer. One remark: one would better not call eigenvectors "unit scaled principal components" for they are in fact unit scaled loadings. They aren't "principal components" (the PC scores) but are principal directions (of the axes of the components). $\endgroup$
    – ttnphns
    Commented Jul 11, 2019 at 21:42
  • $\begingroup$ Good catch. Thank you for pointing that out. To simplify, I removed the definition of eigenvectors. $\endgroup$
    – am313
    Commented Jul 11, 2019 at 22:54
0
$\begingroup$

remark: this is more a comment than an answer.

Here is a script which tries to reproduce, how the Stata solution are calculated.
Indeed it seems to be -different from the solution by SPSS- a transformation on the Eigenvectors (and more specifically: on their Kaiser-normalization over the first 3 eigenvectors) rather than on the PCA-components like in SPSS.

Here is my calculation using my matrix-calculator-software MatMate

;******  MatMate Version 0.1410 Beta *****************************
//  first part of posted data (via clipboard) Eigenvectors (your first protocol)
 // of Stata- computations
clp = csvdatei("clip")
eig_lad = clp[*,1..3]   // the first 3 columns: these are obviously eigenvector-values

        |   0,2700    0,3901     -0,1477 |
        |   0,3298    0,2303     -0,4027 |
        |  -0,3046    0,3149      0,1773 |
        |   0,3489    0,1910      0,0700 |
        |   0,3342    0,2067      0,2720 |
        |  -0,2001    0,4561     -0,1587 |
        |   0,3057    0,3128      0,1531 |
        |  -0,3611    0,2180      0,2913 |
        |   0,2352   -0,2211      0,3662 |
        |  -0,1556    0,3894      0,4578 |
        |   0,3239    0,0525      0,0754 |
        |   0,2091   -0,2445      0,4720 |

uniq = clp[*,4]  // "not-explained" = unique (unexplained by 3 eigenvectors) variances
                 //  (= squared values, not loadings)

        |   0,4779 |
        |   0,3129 |
        |   0,4642 |
        |   0,4715 |
        |   0,4202 |
        |   0,5227 |
        |   0,4728 |
        |   0,3280 |
        |   0,5588 |
        |   0,4457 |
        |   0,5832 |
        |   0,4839 |

//  second part of posted data (via clipboard) : first three eigenvalues
pca_ssl = csvdatei("clip")  // "ssl" means "sum of sqauares of loadings"

        |   3,8723    1,4068      1,1791 |


   // check whether the "unique" variance is really the non-explained
   // variance by the first 3 eigenvectors/PCA-components:
chk = sumzl(    eig_lad ^# 2 *#  pca_ssl   ) + uniq 
   // the squared pca-loadings (eigenvectors^2 scaled by eigenvalues)
   //  plus the unique variance should sum up to 1-variance for each row (=item)

        |   1,0000 |
        |   0,9999 |
        |   1,0000 |
        |   1,0000 |
        |   1,0000 |
        |   1,0001 |
        |   1,0000 |
        |   0,9998 |
        |   0,9999 |
        |   0,9999 |
        |   1,0000 |
        |   1,0000 |




 // get the rotationmatrix to bring the "Kaiser"-normalized loadings to Varimax 
 // note that SPSS computes this based on the PCA-loadings, not on Eigenvector-values 
t = gettrans(normzl(eig_lad ),"varimax") // "normzl(<loadings>)"
                                         // provides Kaiser-normalization per row 
  //       (of course here in Stata based on eigenvectors)

 // rotation-/transformation-matrix "t" from pca to varimax coordinates

        |   0,7942   -0,5573      0,2421 |
        |   0,5724    0,5523     -0,6062 |
        |   0,2041    0,6200      0,7576 |

vmx_lad = eig_lad * t        // this computes the Stata - varimax-coordinates

        |   0,4580   -0,0065     -0,1926 |
        |   0,4012   -0,2970     -0,2735 |
        |  -0,0305    0,4428     -0,1625 |
        |   0,3898   -0,0107      0,1051 |
        |   0,3884    0,1409      0,2402 |
        |   0,1409    0,2473     -0,4385 |
        |   0,4351    0,1348      0,0853 |
        |  -0,1363    0,4913     -0,0528 |
        |   0,0370    0,0087      0,4867 |
        |   0,1310    0,6026      0,0716 |
        |   0,2815   -0,0738      0,1693 |
        |   0,0018    0,0790      0,5657 |

vmx_ssl = sqsumsp((pca_ssl *# sqrt(pca_ssl#)) *t) 
          // sums of squares of the varimax-rotated princ. components (not eigenvectors!)

        |   2,9522    2,0849      1,4207 |


// =============== The SPSS-solution          =========================
eig_lad = clp[*,1..3]   // the first 3 columns: these are obviously eigenvector-values
spss_pca_lad = eig_lad *# sqrt(pca_ssl#) // compute pca-loadings from eigenvectors
spss_t = gettrans(normzl(spss_pca_lad ),"varimax") // "normzl(<pca loadings>)"
spss_vmx_lad = spss_pca_lad * spss_t        // this computes the SPSS - varimax-coordinates

    |   0,7047   -0,0983     -0,1260 |
    |   0,6731   -0,4479     -0,1827 |
    |  -0,2184    0,6266     -0,3091 |
    |   0,6710   -0,1473      0,2377 |
    |   0,6606    0,0244      0,3780 |
    |   0,0474    0,3785     -0,5761 |
    |   0,6989    0,0358      0,1935 |
    |  -0,3776    0,6975     -0,2066 |
    |   0,1846   -0,1019      0,6298 |
    |   0,0624    0,7418     -0,0018 |
    |   0,5276   -0,2131      0,3050 |
    |   0,1273   -0,0160      0,7068 |

   spss_vmx_ssl = sqsumsp(spss_vmx_lad)  // the SPSS-"variances" of the vmx-factors

    |   2,8498    1,8626      1,7455 |

Since all coordinates and also the transformation/rotationmatrix seem to be reproduced correctly it seems this is indeed the internal computation of Stata and also of SPSS

The difference reduced to the syntax/concept would be 

// Stata
eig_lad = clp[*,1..3]   // the first 3 columns: these are obviously eigenvector-values
t = gettrans(normzl(eig_lad ),"varimax") // "normzl(<on eigenvectors>)"
vmx_lad = eig_lad * t        // this computes the Stata - varimax-coordinates

// SPSS
eig_lad = clp[*,1..3]   // the first 3 columns: these are obviously eigenvector-values
spss_pca_lad = eig_lad *# sqrt(pca_ssl#) // compute pca-loadings from eigenvectors
spss_t = gettrans(normzl(spss_pca_lad ),"varimax") // "normzl(<on pca loadings>)"
spss_vmx_lad = spss_pca_lad * spss_t        // this computes the SPSS - varimax-coordinates

The rotation-criterion for the VARIMAX-concept seems to be different in both software-packets. While Stata computes the rotation-angles based on the unit-variance-normalized ("Kaiser-normalized") rows of the eigenvectors , does SPSS compute that rotation-angles based on the unit-variance-normalized ("Kaiser-normalized") rows of PCA-components, which are scalings of the eigenvectors by the square-roots of associated eigenvalues. This should -in most cases - result in different solutions.

Improve this answer
$\endgroup$
0
$\begingroup$

Here is a section from my notes that might help. All normalisations of the eigenvectors (or loadings) are correct! enter image description here

enter image description here

enter image description here

Improve this answer
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.