
                                   xF A C T O R   


                           Unrestricted Factor Analysis 

                    MODULE FOR EXPLORATORY FACTOR ANALYSIS (EFA)

                         Release Version 12.06.07  x64bits
                                    November, 2024

                            Rovira i Virgili University
                                 Tarragona, SPAIN

                                   Programming:
                               Urbano Lorenzo-Seva

                            Mathematical Specification:
                               Urbano Lorenzo-Seva
                                 Pere J. Ferrando

                            Date: Monday, June 23, 2025
                            Time: 12:22:57
--------------------------------------------------------------------------------

DETAILS OF EXPLORATORY FACTOR ANALYSIS

Participants' scores data file                       : C:\Users\Master\Documents\Estudos\Pos-Doc\Segundo Emprico\Banco de Dados\Factor\fscrs.dat
Method to handle missing values                      : Hot-Deck Multiple Imputation in Exploratory Factor Analysis (Lorenzo-Seva & Van Ginkel, 2016)
Missing code value                                   : 999
Number of participants                               : 348
Number of variables                                  : 22
Variables included in the analysis                   : ALL
Variables excluded in the analysis                   : NONE
Number of factors                                    : 2
Number of second order factors                       : 0
Procedure for determining the number of dimensions   : Optimal implementation of Parallel Analysis (PA) (Timmerman, & Lorenzo-Seva, 2011)
Dispersion matrix                                    : Polychoric Correlations
Robust analyses based on bootstrap                   : None
Asymptotic Covariance/Variance matrix                : analytically estimated
Method for factor extraction                         : Robust Diagonally Weighted Least Squares (RDWLS)
Correction for robust Chi square                     : LOSEFER empirical correction (Lorenzo-Seva & Ferrando, 2023)
Rotation to achieve factor simplicity                : Robust Promin (Lorenzo-Seva & Ferrando, 2019b)
Clever rotation start                                : Weighted Varimax
Number of random starts                              : 100
Maximum mumber of iterations                         : 1000
Convergence value                                    : 0.00001000
Factor scores estimates                              : Estimates based on linear model

--------------------------------------------------------------------------------

UNIVARIATE DESCRIPTIVES 

Variable     Mean       Confidence Interval     Variance   Skewness     Kurtosis
                        (95%)                                          (Zero centered)

V   1        1.440     (   1.27     1.61)       1.557      0.530       -0.702
V   2        1.491     (   1.33     1.66)       1.434      0.383       -0.775
V   3        2.940     (   2.79     3.09)       1.229     -0.984        0.356
V   4        1.193     (   1.02     1.36)       1.541      0.815       -0.376
V   5        2.310     (   2.13     2.49)       1.714     -0.249       -1.051
V   6        1.135     (   0.98     1.29)       1.341      0.681       -0.546
V   7        1.290     (   1.12     1.46)       1.499      0.619       -0.636
V   8        2.638     (   2.46     2.82)       1.708     -0.679       -0.636
V   9        0.187     (   0.10     0.27)       0.405      4.049       16.996
V  10        0.149     (   0.07     0.23)       0.363      4.673       22.784
V  11        2.905     (   2.73     3.08)       1.707     -1.181        0.268
V  12        0.816     (   0.65     0.98)       1.472      1.295        0.467
V  13        2.830     (   2.66     3.00)       1.601     -0.917       -0.168
V  14        1.730     (   1.56     1.90)       1.525      0.157       -0.946
V  15        0.299     (   0.19     0.41)       0.629      3.121        9.757
V  16        2.767     (   2.61     2.93)       1.391     -0.753       -0.276
V  17        1.164     (   1.01     1.32)       1.327      0.571       -0.691
V  18        1.905     (   1.74     2.07)       1.436      0.093       -0.781
V  19        3.020     (   2.88     3.16)       1.002     -0.816        0.025
V  20        1.417     (   1.23     1.60)       1.835      0.438       -1.095
V  21        3.201     (   3.07     3.33)       0.862     -1.080        0.685
V  22        0.543     (   0.40     0.69)       1.150      2.053        3.225

Polychoric correlation is advised when the univariate distributions of ordinal items are 
asymmetric or with excess of kurtosis. If both indices are lower than one in absolute value, 
then Pearson correlation is advised. You can read more about this subject in:

Muthén, B., & Kaplan D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189. doi:10.1111/j.2044-8317.1985.tb00832.x
Muthén, B., & Kaplan D. (1992). A comparison of some methodologies for the factor analysis of non-normal Likert variables: A note on the size of the model. British Journal of Mathematical and Statistical Psychology, 45, 19-30. doi:10.1111/j.2044-8317.1992.tb00975.x



BAR CHARTS FOR ORDINAL VARIABLES

Variable    1  

 Value     Freq
                |
     0      99  |  ****************************************
     1      97  |  ***************************************
     2      81  |  ********************************
     3      42  |  ****************
     4      29  |  ***********
                +-----------+---------+---------+-----------+
                 0        24.8       49.5       74.3        99.0

Variable    2  

 Value     Freq
                |
     0      89  |  *************************************
     1      94  |  ****************************************
     2      92  |  ***************************************
     3      51  |  *********************
     4      22  |  *********
                +-----------+---------+---------+-----------+
                 0        23.5       47.0       70.5        94.0

Variable    3  

 Value     Freq
                |
     0      17  |  *****
     1      19  |  *****
     2      66  |  *******************
     3     112  |  *********************************
     4     134  |  ****************************************
                +-----------+---------+---------+-----------+
                 0        33.5       67.0       100.5        134.0

Variable    4  

 Value     Freq
                |
     0     134  |  ****************************************
     1      96  |  ****************************
     2      59  |  *****************
     3      35  |  **********
     4      24  |  *******
                +-----------+---------+---------+-----------+
                 0        33.5       67.0       100.5        134.0

Variable    5  

 Value     Freq
                |
     0      39  |  ******************
     1      61  |  *****************************
     2      84  |  ****************************************
     3      81  |  **************************************
     4      83  |  ***************************************
                +-----------+---------+---------+-----------+
                 0        21.0       42.0       63.0        84.0

Variable    6  

 Value     Freq
                |
     0     140  |  ****************************************
     1      82  |  ***********************
     2      77  |  **********************
     3      37  |  **********
     4      12  |  ***
                +-----------+---------+---------+-----------+
                 0        35.0       70.0       105.0        140.0

Variable    7  

 Value     Freq
                |
     0     120  |  ****************************************
     1      91  |  ******************************
     2      74  |  ************************
     3      42  |  **************
     4      21  |  *******
                +-----------+---------+---------+-----------+
                 0        30.0       60.0       90.0        120.0

Variable    8  

 Value     Freq
                |
     0      36  |  ************
     1      33  |  ***********
     2      68  |  ***********************
     3      95  |  ********************************
     4     116  |  ****************************************
                +-----------+---------+---------+-----------+
                 0        29.0       58.0       87.0        116.0

Variable    9  

 Value     Freq
                |
     0     311  |  ****************************************
     1      22  |  **
     2       5  |  
     3       7  |  
     4       3  |  
                +-----------+---------+---------+-----------+
                 0        77.8       155.5       233.3        311.0

Variable   10  

 Value     Freq
                |
     0     322  |  ****************************************
     1      11  |  *
     2       8  |  
     3       3  |  
     4       4  |  
                +-----------+---------+---------+-----------+
                 0        80.5       161.0       241.5        322.0

Variable   11  

 Value     Freq
                |
     0      41  |  ***********
     1      11  |  **
     2      36  |  *********
     3     112  |  ******************************
     4     148  |  ****************************************
                +-----------+---------+---------+-----------+
                 0        37.0       74.0       111.0        148.0

Variable   12  

 Value     Freq
                |
     0     215  |  ****************************************
     1      43  |  ********
     2      47  |  ********
     3      25  |  ****
     4      18  |  ***
                +-----------+---------+---------+-----------+
                 0        53.8       107.5       161.3        215.0

Variable   13  

 Value     Freq
                |
     0      31  |  ********
     1      21  |  ******
     2      64  |  ******************
     3      92  |  **************************
     4     140  |  ****************************************
                +-----------+---------+---------+-----------+
                 0        35.0       70.0       105.0        140.0

Variable   14  

 Value     Freq
                |
     0      71  |  ****************************
     1      81  |  *********************************
     2      98  |  ****************************************
     3      67  |  ***************************
     4      31  |  ************
                +-----------+---------+---------+-----------+
                 0        24.5       49.0       73.5        98.0

Variable   15  

 Value     Freq
                |
     0     290  |  ****************************************
     1      32  |  ****
     2      13  |  *
     3       6  |  
     4       7  |  
                +-----------+---------+---------+-----------+
                 0        72.5       145.0       217.5        290.0

Variable   16  

 Value     Freq
                |
     0      21  |  *******
     1      31  |  **********
     2      73  |  ************************
     3     106  |  ************************************
     4     117  |  ****************************************
                +-----------+---------+---------+-----------+
                 0        29.3       58.5       87.8        117.0

Variable   17  

 Value     Freq
                |
     0     139  |  ****************************************
     1      70  |  ********************
     2      93  |  **************************
     3      35  |  **********
     4      11  |  ***
                +-----------+---------+---------+-----------+
                 0        34.8       69.5       104.3        139.0

Variable   18  

 Value     Freq
                |
     0      50  |  ****************
     1      77  |  **************************
     2     118  |  ****************************************
     3      62  |  *********************
     4      41  |  *************
                +-----------+---------+---------+-----------+
                 0        29.5       59.0       88.5        118.0

Variable   19  

 Value     Freq
                |
     0       6  |  *
     1      21  |  ******
     2      72  |  ********************
     3     110  |  *******************************
     4     139  |  ****************************************
                +-----------+---------+---------+-----------+
                 0        34.8       69.5       104.3        139.0

Variable   20  

 Value     Freq
                |
     0     131  |  ****************************************
     1      57  |  *****************
     2      74  |  **********************
     3      56  |  *****************
     4      30  |  *********
                +-----------+---------+---------+-----------+
                 0        32.8       65.5       98.3        131.0

Variable   21  

 Value     Freq
                |
     0       4  |  
     1      15  |  ***
     2      53  |  ************
     3     111  |  **************************
     4     165  |  ****************************************
                +-----------+---------+---------+-----------+
                 0        41.3       82.5       123.8        165.0

Variable   22  

 Value     Freq
                |
     0     256  |  ****************************************
     1      39  |  ******
     2      25  |  ***
     3      12  |  *
     4      16  |  **
                +-----------+---------+---------+-----------+
                 0        64.0       128.0       192.0        256.0


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MULTIVARIATE DESCRIPTIVES 

Analysis of the Mardia's (1970) multivariate asymmetry skewness and kurtosis.

                                            Coefficient        Statistic     df       P

Skewness                                        130.660         7578.304   2024     1.0000
SKewness corrected for small sample             130.660         7649.352   2024     1.0000
Kurtosis                                        695.279           48.014            0.0000**

** Significant at 0.05

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STANDARIZED VARIANCE / COVARIANCE MATRIX (POLYCHORIC CORRELATION)
(Polychoric algorithm: Bayes modal estimation; Choi, Kim, Chen, & Dannels, 2011)

Variable     1        2        3        4        5        6        7        8        9       10       11       12       13       14       15       16       17       18       19       20       21       22     
V   1        1.000  
V   2        0.744    1.000  
V   3       -0.184   -0.206    1.000  
V   4        0.519    0.523   -0.143    1.000  
V   5       -0.179   -0.182    0.388   -0.152    1.000  
V   6        0.544    0.489   -0.300    0.518   -0.206    1.000  
V   7        0.596    0.626   -0.291    0.472   -0.132    0.585    1.000  
V   8       -0.215   -0.189    0.449   -0.169    0.377   -0.263   -0.214    1.000  
V   9        0.331    0.347   -0.399    0.339   -0.245    0.502    0.275   -0.362    1.000  
V  10        0.366    0.312   -0.394    0.189   -0.013    0.313    0.545   -0.448    0.602    1.000  
V  11       -0.272   -0.214    0.369   -0.161    0.276   -0.288   -0.256    0.372   -0.313   -0.402    1.000  
V  12        0.426    0.446   -0.286    0.362   -0.237    0.403    0.515   -0.204    0.411    0.527   -0.245    1.000  
V  13       -0.296   -0.228    0.339   -0.179    0.238   -0.314   -0.248    0.376   -0.323   -0.583    0.509   -0.178    1.000  
V  14        0.469    0.392   -0.264    0.386   -0.151    0.339    0.463   -0.191    0.140    0.281   -0.152    0.543   -0.108    1.000  
V  15        0.274    0.307   -0.193    0.241   -0.045    0.332    0.308   -0.170    0.394    0.427   -0.245    0.323   -0.350    0.303    1.000  
V  16       -0.323   -0.282    0.341   -0.270    0.413   -0.326   -0.279    0.419   -0.307   -0.320    0.512   -0.354    0.508   -0.231   -0.359    1.000  
V  17        0.505    0.545   -0.220    0.417   -0.167    0.530    0.531   -0.340    0.241    0.317   -0.284    0.360   -0.364    0.476    0.395   -0.335    1.000  
V  18        0.163    0.129   -0.092    0.201    0.010    0.136    0.203   -0.034    0.100    0.176    0.037    0.133   -0.075    0.396    0.157    0.043    0.275    1.000  
V  19       -0.357   -0.343    0.342   -0.233    0.299   -0.266   -0.318    0.332   -0.367   -0.409    0.452   -0.410    0.423   -0.177   -0.151    0.540   -0.319   -0.103    1.000  
V  20        0.395    0.433   -0.258    0.393   -0.182    0.373    0.365   -0.176    0.269    0.126   -0.115    0.423   -0.168    0.400    0.293   -0.155    0.444    0.183   -0.259    1.000  
V  21       -0.357   -0.329    0.332   -0.220    0.259   -0.318   -0.274    0.472   -0.518   -0.428    0.459   -0.303    0.407   -0.195   -0.192    0.531   -0.313   -0.067    0.565   -0.224    1.000  
V  22        0.433    0.474   -0.311    0.307   -0.283    0.461    0.488   -0.374    0.429    0.416   -0.457    0.376   -0.345    0.261    0.357   -0.436    0.456    0.170   -0.441    0.433   -0.508    1.000  

--------------------------------------------------------------------------------

ADEQUACY OF THE POLYCHORIC CORRELATION MATRIX 

Determinant of the matrix     = 0.000005432401300
Bartlett's statistic          =  3901.0 (df =   231; P = 0.000010)
Kaiser-Meyer-Olkin (KMO) test = 0.74467 (fair)

ITEM LOCATION AND ITEM ADEQUACY INDICES 

Items     QIM      RDI          Normed MSA     

  10      1        0.03736      0.51816 
   9      1        0.04670      0.59228 
  15      1        0.07471      0.84704 
  22      1        0.13578      0.91475 
  12      1        0.20402      0.69367 
   6      1        0.28376      0.69954 
  17      1        0.29095      0.91969 
   4      1        0.29813      0.87761 
   7      1        0.32256      0.66582 
  20      1        0.35417      0.69207 
   1      1        0.35991      0.86672 
   2      1        0.37284      0.82491 
  14      3        0.43247      0.82487 
  18      3        0.47629      0.66159 
   5 **   4        0.57759      0.48317 
   8      4        0.65948      0.67057 
  16      4        0.69181      0.82460 
  13      4        0.70761      0.56939 
  11      4        0.72629      0.92821 
   3      4        0.73491      0.88726 
  19      4        0.75503      0.90635 
  21      4        0.80029      0.87668 

** Number of items proposed to be removed based on MSA: 1


Quartile of Ipsative Means (QIM): The means of the variables are placed in the distribution 
of the average of the values registered for each participant, and the quartile in which 
the means are situated is reported. In a normal-range test, few items should be placed
in the extreme quartiles, whereas most of items should be placed in the central quartiles.

Relative Difficulty Index (RDI): it assesses the position of the items. For a normal-range
test, an optimal pool of items should have about 75% RDI values between .40 and .60 and the
remaining values evenly distributed in both tails.

In test intended for clinical screening or selection purposes, a larger amount of more extreme
items in the appropriate direction is generally recommended.

Measure of Sampling Adequacy (MSA): Values of MSA below .50 suggest that the item does not
measure the same domain as the remaining items in the pool, and so that it should be removed. 

When removing items from the pool, all these aspects should be taken into
account. Sometimes, the conclusion is that new items should be added to the pool
of items.

Lorenzo-Seva, U. & Ferrando, P.J. (2021) MSA: the forgotten index for identifying inappropriate items
            before computing exploratory item factor analysis. Methodology, in press.

--------------------------------------------------------------------------------

EXPLAINED VARIANCE BASED ON EIGENVALUES

Variable  Eigenvalue   Proportion of   Cumulative Proportion
                       Variance        of Variance

   1      7.97015      0.36228         0.36228  
   2      2.36230      0.10738         0.46966  
   3      1.28548      0.05843  
   4      1.14172      0.05190  
   5      0.98480      0.04476  
   6      0.90991      0.04136  
   7      0.85706      0.03896  
   8      0.81893      0.03722  
   9      0.71770      0.03262  
  10      0.63928      0.02906  
  11      0.59492      0.02704  
  12      0.56781      0.02581  
  13      0.51282      0.02331  
  14      0.47640      0.02165  
  15      0.45401      0.02064  
  16      0.39313      0.01787  
  17      0.32156      0.01462  
  18      0.28026      0.01274  
  19      0.25883      0.01176  
  20      0.20939      0.00952  
  21      0.19496      0.00886  
  22      0.04857      0.00221  
       
--------------------------------------------------------------------------------

PARALLEL ANALYSIS (PA) BASED ON MINIMUM RANK FACTOR ANALYSIS
(Timmerman & Lorenzo-Seva, 2011)

Implementation details:

	Correlation matrices analized:                Polychoric correlation matrices
	Number of random correlation matrices:        500
	Method to obtain random correlation matrices: Permutation of the raw data (Buja & Eyuboglu, 1992)


Variable  Real-data      Mean of random   95 percentile of random
          % of variance  % of variance    % of variance

   1       38.1128*       9.4792         10.5923
   2       11.0849*       8.4138          9.3385
   3        6.1114        7.7978          8.5916
   4        5.0388        7.2934          7.9866
   5        4.5133        6.8745          7.4807
   6        4.3102        6.4983          7.0900
   7        3.8939        6.1115          6.5784
   8        3.6535        5.7543          6.1904
   9        3.1716        5.3882          5.7422
  10        2.9328        5.0280          5.3439
  11        2.5838        4.6868          4.9878
  12        2.4022        4.3398          4.6610
  13        2.2024        3.9890          4.3211
  14        2.1291        3.6364          4.0634
  15        1.9914        3.2740          3.8384
  16        1.6938        2.9260          3.5967
  17        1.2499        2.5412          3.3708
  18        1.1367        2.1600          3.1202
  19        0.9487        1.7518          2.8725
  20        0.5476        1.2990          2.4887
  21        0.2909        0.7570          1.7125
       

*  Advised number of dimensions:    2

--------------------------------------------------------------------------------

CLOSENESS TO UNIDIMENSIONALITY ASSESSMENT
Ferrando & Lorenzo-Seva (2018)

ITEM-LEVEL ASSESSMENT

Variable     I-UniCo     I-ECV     IREAL

V   1        0.973      0.807      0.340
V   2        0.948      0.749      0.400
V   3        0.962      0.779      0.271
V   4        0.930      0.716      0.344
V   5        0.879      0.648      0.281
V   6        0.994      0.903      0.224
V   7        0.965      0.786      0.372
V   8        0.863      0.630      0.407
V   9        0.995      0.907      0.205
V  10        0.994      0.903      0.221
V  11        0.874      0.643      0.403
V  12        0.996      0.918      0.196
V  13        0.866      0.634      0.443
V  14        0.870      0.638      0.405
V  15        1.000      0.991      0.048
V  16        0.926      0.710      0.400
V  17        0.991      0.881      0.243
V  18        0.579      0.415      0.273
V  19        0.976      0.819      0.286
V  20        0.923      0.706      0.344
V  21        0.956      0.764      0.346
V  22        1.000      0.994      0.055


OVERALL ASSESSMENT

UniCo  =   0.930
ECV    =   0.782
MIREAL =   0.296

A value of UniCo (Unidimensional Congruence) and I-Unico (Item Unidimensional Congruence) larger than 0.95 suggests that data can be treated as essentially unidimensional. 
A value of ECV (Explained Common Variance) and I-ECV (Item Explained Common Variance) larger than 0.85 suggests that data can be treated as essentially unidimensional.
A value of MIREAL (Mean of Item REsidual Absolute Loadings) and I-REAL (Item REsidual Absolute Loadings) lower than 0.300 suggests that data can be treated as essentially unidimensional.

--------------------------------------------------------------------------------

ROBUST GOODNESS OF FIT STATISTICS 

                         Root Mean Square Error of Approximation (RMSEA) =   0.034; (between 0.010 and 0.050 : close)
                                Estimated Non-Centrality Parameter (NCP) = 163.090
                                                      Degrees of Freedom = 188
                                                      Test of Approximate Fit
                                                      H0 : RMSEA < 0.05;  P = 1.000

             Minimum Fit Function Chi Square with 188 degrees of freedom = 194.026 (P = 0.366699)
Robust Mean and Variance-Adjusted Chi Square with 188 degrees of freedom = 263.566 (P = 0.000231)
           Chi-Square for independence model with 231 degrees of freedom = 7159.867

                             Non-Normed Fit Index (NNFI; Tucker & Lewis) =   0.987
                                             Comparative Fit Index (CFI) =   0.989; (between 0.950 and 0.990 : close)
                          Schwarzs Bayesian Information Criterion (BIC) = 649.812

                                             Goodness of Fit Index (GFI) =   1.000
                                   Adjusted Goodness of Fit Index (AGFI) =   1.000
                     Goodness of Fit Index without diagonal values (GFI) =   1.000
            Adjusted Goodness of Fit Index without diagonal values(AGFI) =   1.000

EIGENVALUES OF THE REDUCED CORRELATION MATRIX

Variable  Eigenvalue 

   1      7.439512792 
   2      1.816871426 
   3      0.672491208 
   4      0.515010056 
   5      0.392366350 
   6      0.328445857 
   7      0.235686853 
   8      0.214468983 
   9      0.141207918 
  10      0.080535157 
  11      0.034368146 
  12      0.001230919 
  13     -0.068488399 
  14     -0.077304294 
  15     -0.099108456 
  16     -0.146768284 
  17     -0.198901913 
  18     -0.260854059 
  19     -0.309608385 
  20     -0.362805797 
  21     -0.395563495 
  22     -0.488093516 
       
--------------------------------------------------------------------------------

UNROTATED LOADING MATRIX 

Variable     F   1    F   2    Communality

V   1        0.752    0.313       0.663
V   2        0.744    0.360       0.684
V   3       -0.483    0.285       0.315
V   4        0.572    0.294       0.414
V   5       -0.368    0.274       0.211
V   6        0.673    0.151       0.476
V   7        0.717    0.273       0.588
V   8       -0.489    0.391       0.392
V   9        0.592   -0.183       0.384
V  10        0.682   -0.229       0.517
V  11       -0.518    0.443       0.465
V  12        0.635    0.131       0.420
V  13       -0.541    0.404       0.456
V  14        0.551    0.311       0.400
V  15        0.469    0.016       0.220
V  16       -0.595    0.426       0.536
V  17        0.673    0.175       0.483
V  18        0.226    0.222       0.100
V  19       -0.577    0.324       0.438
V  20        0.518    0.221       0.317
V  21       -0.599    0.381       0.504
V  22        0.684   -0.113       0.481

--------------------------------------------------------------------------------

WEIGHTS OF ROBUST ROTATION
Lorenzo-Seva, U. & Ferrando, P.J. (2019b)

Variable     h       w

V   1       1.4210   0.5519
V   2       1.3827   0.5639
V   3       1.6419   0.4822
V   4       1.5900   0.4986
V   5       1.6698   0.4734
V   6       1.4775   0.5340
V   7       1.3771   0.5657
V   8       1.6197   0.4892
V   9       2.5927   0.1823
V  10       3.1707   0.0000
V  11       1.6966   0.4649
V  12       1.6433   0.4818
V  13       1.5715   0.5044
V  14       1.5323   0.5167
V  15       2.3480   0.2595
V  16       1.6612   0.4761
V  17       1.5601   0.5080
V  18       1.6951   0.4654
V  19       1.5499   0.5112
V  20       1.5311   0.5171
V  21       1.4796   0.5334
V  22       1.9543   0.3837

h: average of the diagonal values in the asymptotic variance/covariance matrix for each variable.
w: Robust weight value for each variable.

Interpretation guide: The variable with the less stable set of correlations (i.e., a large value of h)
     will have a weight value (w) close to zero. In the other hand, a variable with a set of perfectly
     stable correlations will  have a weight value (w) of one.  The largest the value in w,  the  most
     important the variable is order to define the simple structure of the factor solution.

--------------------------------------------------------------------------------

SEMI-SPECIFIED TARGET LOADING MATRIX
Obtained from prerotation of the loading matrix

Variable     F   1    F   2 

V   1         ---     0.000  
V   2         ---     0.000  
V   3        0.000     ---   
V   4         ---     0.000  
V   5        0.000     ---   
V   6         ---     0.000  
V   7         ---     0.000  
V   8        0.000     ---   
V   9        0.000     ---   
V  10        0.000     ---   
V  11        0.000     ---   
V  12         ---     0.000  
V  13        0.000     ---   
V  14         ---     0.000  
V  15         ---     0.000  
V  16        0.000     ---   
V  17         ---     0.000  
V  18         ---     0.000  
V  19        0.000     ---   
V  20         ---     0.000  
V  21        0.000     ---   
V  22        0.000    0.000  

--------------------------------------------------------------------------------

ROTATED LOADING MATRIX 

Variable     F   1    F   2 

V   1        0.848    0.054  
V   2        0.895    0.114  
V   3       -0.005    0.558  
V   4        0.707    0.110  
V   5        0.061    0.495  
V   6        0.615   -0.110  
V   7        0.780    0.021  
V   8        0.109    0.690  
V   9        0.189   -0.482  
V  10        0.199   -0.577  
V  11        0.149    0.767  
V  12        0.568   -0.117  
V  13        0.089    0.728  
V  14        0.712    0.139  
V  15        0.330   -0.185  
V  16        0.079    0.779  
V  17        0.641   -0.081  
V  18        0.397    0.173  
V  19       -0.024    0.647  
V  20        0.590    0.044  
V  21        0.026    0.726  
V  22        0.330   -0.436  

ROTATED LOADING MATRIX 
(loadings lower than absolute   0.300 omitted)

Variable     F   1    F   2 

V   1        0.848           
V   2        0.895           
V   3                 0.558  
V   4        0.707           
V   5                 0.495  
V   6        0.615           
V   7        0.780           
V   8                 0.690  
V   9                -0.482  
V  10                -0.577  
V  11                 0.767  
V  12        0.568           
V  13                 0.728  
V  14        0.712           
V  15        0.330           
V  16                 0.779  
V  17        0.641           
V  18        0.397           
V  19                 0.647  
V  20        0.590           
V  21                 0.726  
V  22        0.330   -0.436  

EXPLAINED VARIANCE OF ROTATED FACTORS AND RELIABILITY OF PHI-INFORMATION OBLIQUE EAP SCORES
Ferrando & Lorenzo-Seva (2016)

Factor      Variance   ORION             Factor Determinacy Index
                                               

   1         4.979     0.921             0.960
   2         4.485     0.899             0.948


The appropriate implementation of EAP score estimation in factor model involves to obtain 
point estimates that make use of the full prior information (in particular the inter-factor 
correlation matrix), and to complement the point estimates with measures of the reliability of 
these estimates. In order to achieve it, FACTOR computes: (1) the EAP score estimation named 
'Fully-Informative Prior Oblique EAP scores'; and (2) the reliability estimates named ORION
(acronim for 'Overall Reliability of fully-Informative prior Oblique N-EAP scores').
See Ferrando & Lorenzo-Seva (2016) for further details.

--------------------------------------------------------------------------------

INDICES OF FACTOR SIMPLICITY
Bentler (1977) & Lorenzo-Seva (2003) 

Bentler's simplicity index (S) =   0.99732 (Percentile 100)
Loading simplicity index  (LS) =   0.57712 (Percentile 100)

       
--------------------------------------------------------------------------------

INTER-FACTORS CORRELATION MATRIX

Factor       F   1    F   2 

   1         1.000  
   2        -0.636    1.000  

--------------------------------------------------------------------------------

STRUCTURE MATRIX 

Variable     F   1    F   2 

V   1        0.813   -0.485  
V   2        0.822   -0.454  
V   3       -0.359    0.561  
V   4        0.638   -0.340  
V   5       -0.254    0.457  
V   6        0.685   -0.501  
V   7        0.767   -0.475  
V   8       -0.329    0.620  
V   9        0.496   -0.602  
V  10        0.565   -0.703  
V  11       -0.338    0.672  
V  12        0.642   -0.478  
V  13       -0.374    0.672  
V  14        0.623   -0.313  
V  15        0.447   -0.394  
V  16       -0.417    0.729  
V  17        0.692   -0.488  
V  18        0.287   -0.080  
V  19       -0.435    0.662  
V  20        0.562   -0.331  
V  21       -0.436    0.710  
V  22        0.607   -0.645  

--------------------------------------------------------------------------------

PRATT'S IMPORTANCE MEASURES
Wu & Zumbo (2017)

UNSTANDARDIZED PRATT'S MEAURES (ETA-SQUARED)

Variable     F   1    F   2 

V   1        0.663    0.000  
V   2        0.684    0.000  
V   3        0.002    0.313  
V   4        0.414    0.000  
V   5        0.000    0.211  
V   6        0.421    0.055  
V   7        0.588    0.000  
V   8        0.000    0.392  
V   9        0.094    0.290  
V  10        0.112    0.405  
V  11        0.000    0.465  
V  12        0.364    0.056  
V  13        0.000    0.456  
V  14        0.400    0.000  
V  15        0.147    0.073  
V  16        0.000    0.536  
V  17        0.444    0.039  
V  18        0.100    0.000  
V  19        0.010    0.428  
V  20        0.317    0.000  
V  21        0.000    0.504  
V  22        0.200    0.281  

COMMUNALITY-STANDARDIZED PRATT'S MEASURES

Variable     F   1    F   2 

V   1        1.000    0.000  
V   2        1.000    0.000  
V   3        0.005    0.995  
V   4        1.000    0.000  
V   5        0.000    1.000  
V   6        0.885    0.115  
V   7        1.000    0.000  
V   8        0.000    1.000  
V   9        0.245    0.755  
V  10        0.217    0.783  
V  11        0.000    1.000  
V  12        0.867    0.133  
V  13        0.000    1.000  
V  14        1.000    0.000  
V  15        0.669    0.331  
V  16        0.000    1.000  
V  17        0.919    0.081  
V  18        1.000    0.000  
V  19        0.024    0.976  
V  20        1.000    0.000  
V  21        0.000    1.000  
V  22        0.416    0.584  

UNIQUE DIRECTIONAL CORRELATION (ETA)

Variable     F   1    F   2 

V   1        0.814    0.000  
V   2        0.827    0.000  
V   3        0.041    0.559  
V   4        0.643    0.000  
V   5        0.000    0.459  
V   6        0.649    0.234  
V   7        0.767    0.000  
V   8        0.000    0.626  
V   9        0.306    0.539  
V  10        0.335    0.637  
V  11        0.000    0.682  
V  12        0.604    0.236  
V  13        0.000    0.675  
V  14        0.633    0.000  
V  15        0.384    0.270  
V  16        0.000    0.732  
V  17        0.666    0.198  
V  18        0.317    0.000  
V  19        0.102    0.654  
V  20        0.563    0.000  
V  21        0.000    0.710  
V  22        0.447    0.530  

Importance measures indicate the proportions of the variation in each observed
indicator that are attributable to the factors (an interpretation analogous to
the effect size measure of eta-squared). Eta correlations is a measure of unique
directional correlation of each factor with an observed indicator.

--------------------------------------------------------------------------------

CONSTRUCT REPLICABILITY: GENERALIZED H (G-H) INDEX
Ferrando & Lorenzo-Seva (2018) 

Factor       H-Latent        H-Observed

F   1        0.921           0.922  
F   2        0.899           0.927  


The H index evaluates how well a set of items represents a common factor. It is bounded between 0 and 1 and approaches unity as 
the magnitude of the factor loadings and/or the number of items increase. High H values (>.80) suggest a well defined latent variable,
which is more likely to be stable across studies, whereas low H values suggest a poorly defined latent variable, which is likely
to change across studies.

H-Latent assesses how well the factor can be identified by the continuous latent response variables that underlie the observed item scores,
whereas H-Observed assesses how well it can be identified from the observed item scores.

--------------------------------------------------------------------------------

QUALITY AND EFFECTIVENESS OF FACTOR SCORE ESTIMATES 

Ferrando & Lorenzo-Seva (2018)

                                                    F  1      F  2   

Factor Determinacy Index (FDI)                      0.960     0.948 
ORION marginal reliability                          0.921     0.899 
Sensitivity ratio (SR)                              3.410     2.985 
Expected percentage of true differences (EPTD)      93.5%     92.5% 

The sensitivity ratio (SR) can be interpreted as the number of different factor levels than can be differentiated
on the basis of the factor score estimates. The expected percentage of true differences (EPTD) is the estimated
percentage of differences between the observed factor score estimates that are in the same direction as the
corresponding true differences.

If factor scores are to be used for individual assessment, FDI values above .90, marginal reliabilities above .80,
SR above 2, and EPTDs above 90% are recommended.


--------------------------------------------------------------------------------

ITEM RESPONSE THEORY PARAMETERIZATION: MULTIDIMENSIONAL NORMAL-OGIVE GRADED RESPONSE MODEL
Reckase's parameterization (Reckase, 1985)

PATTERN OF ITEM DISCRIMINATIONS

Item         a   1    a   2    MDISC    

V   1        1.460    0.093    1.463  
V   2        1.590    0.203    1.603  
V   3       -0.006    0.674    0.674  
V   4        0.924    0.143    0.935  
V   5        0.068    0.557    0.562  
V   6        0.850   -0.151    0.863  
V   7        1.216    0.033    1.216  
V   8        0.140    0.885    0.896  
V   9        0.241   -0.614    0.659  
V  10        0.286   -0.830    0.878  
V  11        0.204    1.048    1.068  
V  12        0.745   -0.153    0.761  
V  13        0.121    0.988    0.995  
V  14        0.919    0.180    0.937  
V  15        0.373   -0.209    0.428  
V  16        0.116    1.144    1.150  
V  17        0.892   -0.112    0.899  
V  18        0.419    0.182    0.457  
V  19       -0.032    0.863    0.863  
V  20        0.714    0.053    0.716  
V  21        0.037    1.032    1.032  
V  22        0.458   -0.605    0.758  

    a: item discrimination in each dimension
MDISC: item multidimensional discrimination


CATEGORY INTERCEPTS

Item         d   1    d   2    d   3    d   4 

V   1       -0.981    0.274    1.425    2.383  
V   2       -1.167    0.115    1.435    2.717  
V   3       -2.000   -1.524   -0.657    0.353  
V   4       -0.382    0.542    1.248    1.937  
V   5       -1.368   -0.632    0.081    0.800  
V   6       -0.342    0.488    1.488    2.513  
V   7       -0.622    0.420    1.420    2.418  
V   8       -1.619   -1.087   -0.346    0.552  
V   9        1.588    2.186    2.420    3.034  
V  10        2.075    2.470    2.953    3.273  
V  11       -1.621   -1.420   -0.910    0.258  
V  12        0.394    0.850    1.520    2.138  
V  13       -1.826   -1.409   -0.584    0.335  
V  14       -1.068   -0.205    0.746    1.739  
V  15        1.095    1.632    2.018    2.323  
V  16       -2.277   -1.525   -0.529    0.621  
V  17       -0.354    0.354    1.553    2.584  
V  18       -1.122   -0.364    0.565    1.250  
V  19       -2.821   -1.897   -0.760    0.340  
V  20       -0.381    0.122    0.827    1.651  
V  21       -3.230   -2.275   -1.161    0.092  
V  22        0.874    1.425    1.946    2.339  


--------------------------------------------------------------------------------

DISTRIBUTION OF RESIDUALS 

Number of Residuals = 231

Summary Statistics for Fitted Residuals

Smallest Fitted Residual = -0.1765
  Median Fitted Residual = -0.0005
 Largest Fitted Residual =  0.3005
    Mean Fitted Residual =  0.0028
Variance Fitted Residual =  0.0043

               Root Mean Square of Residuals (RMSR) =  0.0658
Expected mean value of RMSR for an acceptable model =  0.0537 (Kelley's criterion) (Kelley, 1935,page 13; see also Harman, 1962, page 21 of the 2nd edition)
Note: if the value of RMSR is much larger than Kelley's criterion value the model cannot be considered as good

          Weighted Root Mean Square Residual (WRMR) =  0.0492 (values under 1.0 have been recommended to represent good fit; Yu & Muthen, 2002)

Histogram for fitted residuals


 Value           Freq
                      |
   -0.1765         1  |  
   -0.1288         8  |  ****
   -0.0811        29  |  ****************
   -0.0334        60  |  **********************************
    0.0143        70  |  ****************************************
    0.0620        42  |  ************************
    0.1097        16  |  *********
    0.1574         2  |  *
    0.2051         2  |  *
    0.2528         0  |  
    0.3005         1  |  
                      +-----------+---------+---------+-----------+
                      0       17.5      35.0      52.5       70.0


Summary Statistics for Standardized Residuals

Smallest Standardized Residual = -3.29
  Median Standardized Residual = -0.01
 Largest Standardized Residual =  5.60
    Mean Standardized Residual =  0.05

Note: Large estimated residual correlations are observed.
      You should consider computing MORGANA Factor Analysis
       
Stemleaf Plot for Standardized Residuals

  -3 | 3
  -2 | 54433211
  -1 | 9997776666555444443333322222110000000
  -0 | 9999988888877777776666665555555444444444433333332222222222211111111
   0 | 000000001111111111222222233333333344445555555666666666677889999999
   1 | 0000000000011111122333344444556777899
   2 | 111223334589
   3 | 48
   4 | 
   5 | 6


Largest Negative Standardized Residuals

Residual for Var  20 and Var  10    -3.29

Largest Positive Standardized Residuals

Residual for Var  10 and Var   5     5.60
Residual for Var  10 and Var   9     2.92
Residual for Var  16 and Var  10     3.42
Residual for Var  14 and Var  12     2.84
Residual for Var  18 and Var  14     3.77
       
--------------------------------------------------------------------------------

DESCRIPTIVES RELATED TO MISSING DATA

 Missing value code           :       999

 No missing data was observed in your data

--------------------------------------------------------------------------------

PARTICIPANTS' SCORE ESTIMATES ON FACTORS: ORION EAP scores

Ferrando & Lorenzo-Seva (2016) 

Model to estime factor scores: linear aproximation 
ORION scores saved in file: ORION_scores.dat 

Case         Factor    
              1       2    

   1        -0.477   0.181  
   2         0.516   0.457  
   3         0.597  -0.581  
   4        -0.120   0.062  
   5        -0.230   0.345  
   6         0.132   0.524  
   7         0.253   0.393  
   8        -0.812   0.568  
   9         0.510  -0.484  
  10         0.498  -0.512  
  11         0.382  -0.206  
  12         0.041   0.132  
  13        -0.561   0.448  
  14         0.671   0.021  
  15        -0.185  -0.537  
  16         0.096   0.119  
  17        -0.124  -0.309  
  18         0.511   0.386  
  19         0.395   0.513  
  20        -0.166   0.271  
  21         0.064  -0.338  
  22         0.369  -0.228  
  23         1.540  -1.342  
  24        -0.335  -0.103  
  25         0.229  -0.033  
  26        -0.220  -0.125  
  27         0.386  -0.373  
  28         0.105  -0.743  
  29        -1.131   0.540  
  30        -0.288   0.724  
  31         0.257  -0.034  
  32         0.076  -0.276  
  33         0.144   0.106  
  34        -1.024   0.529  
  35         0.329   0.343  
  36        -0.523  -0.079  
  37         0.331  -0.077  
  38        -0.389  -0.420  
  39         0.335  -0.571  
  40        -0.511   0.485  
  41        -0.369   0.858  
  42         0.156   0.148  
  43        -0.177  -0.288  
  44        -0.406  -0.020  
  45         0.305   0.404  
  46        -0.555   0.411  
  47        -0.099  -0.102  
  48        -1.413   0.342  
  49         0.184   0.389  
  50        -0.537   1.053  
  51         0.599  -0.258  
  52        -0.605   0.252  
  53         0.845   0.215  
  54        -0.150  -0.033  
  55         0.474  -0.198  
  56         0.192  -0.753  
  57        -0.789  -0.553  
  58         0.301   0.646  
  59         0.593   0.290  
  60         0.331  -0.218  
  61         0.782  -0.133  
  62         0.158  -0.212  
  63         0.637  -0.050  
  64        -0.841   1.047  
  65         0.641   0.007  
  66        -0.345  -0.162  
  67        -0.439   0.836  
  68        -0.580   0.203  
  69         1.860  -1.337  
  70         0.103  -0.059  
  71         0.140  -0.456  
  72        -0.481  -0.157  
  73        -0.115  -0.229  
  74        -0.181   0.467  
  75        -0.990  -0.209  
  76         0.641  -0.615  
  77         0.215  -0.349  
  78        -0.381   0.249  
  79        -0.006  -0.175  
  80        -1.058  -0.644  
  81        -0.048  -0.213  
  82        -0.173   0.011  
  83        -0.327   0.011  
  84         0.381   0.222  
  85         0.170   0.194  
  86        -0.184   0.427  
  87         0.816   0.355  
  88        -0.381  -0.141  
  89        -1.004   0.197  
  90         0.961  -0.595  
  91         0.359  -0.298  
  92        -0.465   0.208  
  93        -0.922  -0.194  
  94        -0.791  -1.119  
  95         1.106  -0.272  
  96        -0.773   0.545  
  97        -0.410  -0.274  
  98         0.212  -0.048  
  99         0.491  -0.161  
 100         1.210  -0.563  
 101        -1.020   0.451  
 102        -0.697   0.182  
 103        -0.198   0.269  
 104        -0.538   1.362  
 105         0.282  -0.311  
 106        -0.800  -0.248  
 107         0.390  -0.668  
 108        -0.835  -0.506  
 109        -0.979   1.116  
 110        -0.700   0.731  
 111         0.040  -0.151  
 112        -0.434  -0.019  
 113         0.096   0.718  
 114        -0.302   0.017  
 115         0.494  -0.326  
 116         0.195   0.976  
 117        -1.424   0.432  
 118        -0.558   0.798  
 119        -1.408   0.654  
 120         0.473  -0.715  
 121         0.563   0.815  
 122         0.272   0.891  
 123        -0.512   0.847  
 124         0.690  -1.377  
 125        -0.710   0.406  
 126        -0.213   0.529  
 127        -0.533   0.635  
 128        -0.654   0.327  
 129        -0.906  -0.118  
 130         0.643   0.653  
 131         0.175  -0.109  
 132        -0.425   0.484  
 133         0.799  -0.659  
 134        -0.270  -0.234  
 135         0.434  -0.192  
 136         0.020   1.130  
 137        -0.777   0.309  
 138        -0.077   0.635  
 139         0.695  -0.273  
 140        -0.454  -0.506  
 141         1.066  -1.961  
 142        -0.212   0.698  
 143        -0.195  -1.030  
 144        -1.012   0.991  
 145        -0.551   0.045  
 146         0.598  -0.277  
 147        -0.927   0.103  
 148        -0.794   0.636  
 149        -0.246   0.891  
 150        -0.767   0.858  
 151         0.255   0.528  
 152        -0.409  -0.060  
 153        -0.450   1.253  
 154        -1.111   0.961  
 155        -0.619   0.602  
 156        -0.155   0.043  
 157        -1.038   0.894  
 158        -0.966   0.906  
 159        -0.669   0.664  
 160         0.755  -0.396  
 161         1.123  -0.127  
 162        -0.121   0.100  
 163        -0.992  -0.038  
 164         0.719  -0.282  
 165        -0.782   0.732  
 166         0.714  -0.347  
 167        -0.770   0.616  
 168        -0.557   0.694  
 169        -1.090  -0.675  
 170        -0.807   0.879  
 171         1.080  -0.339  
 172        -0.271  -0.393  
 173        -1.010   1.168  
 174         0.090   0.397  
 175         0.419  -0.272  
 176        -1.125   1.176  
 177        -0.925   1.040  
 178        -0.226   0.004  
 179        -1.041   1.069  
 180        -1.220   0.952  
 181        -0.001   0.809  
 182         1.478   0.463  
 183        -0.646   0.844  
 184         0.663  -0.508  
 185        -0.846   1.002  
 186        -0.800   0.320  
 187        -0.426   0.430  
 188        -0.375   1.260  
 189        -0.957   0.636  
 190        -1.175   1.197  
 191         0.492   0.084  
 192        -1.401   1.167  
 193        -1.074   1.053  
 194        -0.704   0.552  
 195        -1.188   1.173  
 196         0.765  -0.853  
 197        -0.237   0.764  
 198        -0.769   0.846  
 199        -0.069   0.043  
 200        -0.936   1.070  
 201        -1.028   1.119  
 202         0.146   0.104  
 203        -0.644   0.876  
 204         1.375  -1.875  
 205        -1.231   0.975  
 206        -0.869   0.355  
 207         0.789   0.218  
 208        -0.571   0.541  
 209        -0.973   1.035  
 210        -1.308   1.036  
 211         0.029  -0.489  
 212        -1.329   1.147  
 213        -1.329   1.147  
 214        -0.331   1.685  
 215        -0.552   0.769  
 216        -1.180  -0.362  
 217        -0.296   0.331  
 218        -0.468   0.521  
 219         0.923  -1.154  
 220        -0.321  -0.642  
 221        -0.000   1.072  
 222         0.327   0.185  
 223        -0.405   0.753  
 224         0.334  -0.573  
 225         1.354  -1.319  
 226        -0.729   1.276  
 227        -0.137   0.211  
 228        -0.486   1.163  
 229         0.296   0.003  
 230         1.052  -3.031  
 231         0.079  -1.541  
 232         0.755  -0.973  
 233        -0.968   2.319  
 234        -1.352   1.114  
 235        -1.352   1.114  
 236         0.804   0.083  
 237         1.433  -0.745  
 238        -1.146   0.417  
 239         0.433   0.478  
 240         1.044  -0.252  
 241        -0.790   0.854  
 242        -0.698   0.574  
 243         0.485  -1.058  
 244         0.862   0.101  
 245        -0.209   0.754  
 246         0.621   0.738  
 247        -1.261   1.245  
 248        -1.261   1.245  
 249         0.108   0.677  
 250        -0.119  -0.296  
 251         1.902  -0.151  
 252         0.086   0.951  
 253        -1.043   0.147  
 254        -1.231   0.138  
 255         0.070   0.020  
 256         0.216   0.758  
 257         0.006   1.377  
 258        -0.866  -0.003  
 259         1.106  -4.047  
 260        -0.898   1.237  
 261        -1.449   0.141  
 262         1.388   1.100  
 263         0.908  -0.579  
 264        -0.309   0.482  
 265        -0.235   0.270  
 266        -0.075  -0.953  
 267        -0.191   0.168  
 268        -0.935   0.600  
 269         1.227  -0.551  
 270         0.597  -0.241  
 271        -1.189   0.435  
 272        -0.718  -0.123  
 273         0.975  -0.581  
 274         2.324  -3.283  
 275         1.222  -0.170  
 276        -0.160  -0.194  
 277         0.904  -0.705  
 278        -0.743   0.230  
 279         0.967  -0.218  
 280         1.129  -0.839  
 281         1.061   0.949  
 282        -0.476   0.402  
 283        -1.087  -0.478  
 284         1.815  -1.047  
 285         0.299  -0.365  
 286         0.838  -0.486  
 287        -1.604   0.258  
 288        -1.389  -0.963  
 289        -0.698   0.297  
 290         0.110  -0.279  
 291         2.097  -3.844  
 292        -0.989  -0.071  
 293         0.259   0.875  
 294        -0.435   0.063  
 295         1.338  -1.069  
 296        -1.256   0.250  
 297        -1.696  -0.185  
 298        -0.938   0.552  
 299         1.384   0.197  
 300        -0.922   0.378  
 301         1.362  -0.673  
 302        -0.656   1.058  
 303         0.845   0.139  
 304        -0.324  -0.609  
 305         2.761  -2.959  
 306         3.442  -3.008  
 307         1.916   0.241  
 308        -1.108   1.125  
 309        -0.873   1.480  
 310        -0.825  -0.074  
 311         2.328  -0.617  
 312         0.693  -1.097  
 313         2.223  -1.347  
 314         0.065  -0.161  
 315        -0.846  -0.950  
 316        -0.962   0.376  
 317         2.044  -0.388  
 318         0.252  -0.192  
 319         1.772  -0.901  
 320        -0.017   1.359  
 321         3.218  -3.497  
 322        -0.466  -0.602  
 323         0.057  -1.603  
 324         1.444  -1.543  
 325         0.356   0.833  
 326         1.356   0.237  
 327         0.292  -0.071  
 328        -0.532  -0.273  
 329        -0.113  -0.129  
 330        -1.600   0.715  
 331        -1.050   1.300  
 332         0.128  -0.570  
 333         0.380  -0.052  
 334        -0.554  -0.415  
 335         1.111  -2.029  
 336         2.383  -0.960  
 337         1.486  -2.150  
 338         0.783   0.468  
 339         3.972  -4.457  
 340         1.819  -1.210  
 341         0.667  -0.426  
 342         0.368  -1.501  
 343         1.054  -2.367  
 344         1.240  -1.780  
 345         2.418  -3.792  
 346         0.129   0.643  
 347         0.204  -0.336  
 348         3.828  -4.695  


ACCURACY OF FACTOR SCORE ESTIMATES 

FACTOR:  1 

Case         Approximate 95%       Posterior   
             confidence interval   SE         

   1        (-0.940  -0.014)       0.281
   2        ( 0.054   0.979)       0.281
   3        ( 0.134   1.059)       0.281
   4        (-0.583   0.343)       0.281
   5        (-0.693   0.233)       0.281
   6        (-0.331   0.595)       0.281
   7        (-0.210   0.716)       0.281
   8        (-1.275  -0.350)       0.281
   9        ( 0.048   0.973)       0.281
  10        ( 0.035   0.961)       0.281
  11        (-0.081   0.845)       0.281
  12        (-0.422   0.503)       0.281
  13        (-1.024  -0.098)       0.281
  14        ( 0.208   1.134)       0.281
  15        (-0.648   0.278)       0.281
  16        (-0.367   0.559)       0.281
  17        (-0.587   0.339)       0.281
  18        ( 0.048   0.973)       0.281
  19        (-0.068   0.858)       0.281
  20        (-0.629   0.297)       0.281
  21        (-0.399   0.527)       0.281
  22        (-0.094   0.832)       0.281
  23        ( 1.077   2.003)       0.281
  24        (-0.798   0.128)       0.281
  25        (-0.233   0.692)       0.281
  26        (-0.682   0.243)       0.281
  27        (-0.077   0.849)       0.281
  28        (-0.358   0.568)       0.281
  29        (-1.594  -0.668)       0.281
  30        (-0.751   0.174)       0.281
  31        (-0.206   0.720)       0.281
  32        (-0.387   0.539)       0.281
  33        (-0.319   0.607)       0.281
  34        (-1.487  -0.561)       0.281
  35        (-0.134   0.792)       0.281
  36        (-0.986  -0.061)       0.281
  37        (-0.132   0.794)       0.281
  38        (-0.852   0.074)       0.281
  39        (-0.128   0.798)       0.281
  40        (-0.973  -0.048)       0.281
  41        (-0.832   0.094)       0.281
  42        (-0.307   0.618)       0.281
  43        (-0.640   0.286)       0.281
  44        (-0.869   0.057)       0.281
  45        (-0.157   0.768)       0.281
  46        (-1.017  -0.092)       0.281
  47        (-0.562   0.364)       0.281
  48        (-1.876  -0.950)       0.281
  49        (-0.279   0.647)       0.281
  50        (-1.000  -0.074)       0.281
  51        ( 0.136   1.062)       0.281
  52        (-1.068  -0.142)       0.281
  53        ( 0.382   1.308)       0.281
  54        (-0.613   0.313)       0.281
  55        ( 0.011   0.937)       0.281
  56        (-0.271   0.655)       0.281
  57        (-1.252  -0.326)       0.281
  58        (-0.162   0.764)       0.281
  59        ( 0.130   1.056)       0.281
  60        (-0.132   0.794)       0.281
  61        ( 0.319   1.245)       0.281
  62        (-0.304   0.621)       0.281
  63        ( 0.174   1.100)       0.281
  64        (-1.303  -0.378)       0.281
  65        ( 0.178   1.103)       0.281
  66        (-0.808   0.117)       0.281
  67        (-0.902   0.024)       0.281
  68        (-1.043  -0.117)       0.281
  69        ( 1.397   2.323)       0.281
  70        (-0.360   0.566)       0.281
  71        (-0.323   0.603)       0.281
  72        (-0.943  -0.018)       0.281
  73        (-0.578   0.348)       0.281
  74        (-0.644   0.281)       0.281
  75        (-1.453  -0.527)       0.281
  76        ( 0.178   1.104)       0.281
  77        (-0.248   0.678)       0.281
  78        (-0.844   0.081)       0.281
  79        (-0.469   0.457)       0.281
  80        (-1.521  -0.595)       0.281
  81        (-0.511   0.415)       0.281
  82        (-0.636   0.290)       0.281
  83        (-0.790   0.136)       0.281
  84        (-0.082   0.844)       0.281
  85        (-0.293   0.632)       0.281
  86        (-0.647   0.279)       0.281
  87        ( 0.353   1.279)       0.281
  88        (-0.844   0.082)       0.281
  89        (-1.467  -0.541)       0.281
  90        ( 0.499   1.424)       0.281
  91        (-0.104   0.822)       0.281
  92        (-0.928  -0.002)       0.281
  93        (-1.385  -0.459)       0.281
  94        (-1.254  -0.328)       0.281
  95        ( 0.643   1.568)       0.281
  96        (-1.236  -0.310)       0.281
  97        (-0.873   0.053)       0.281
  98        (-0.251   0.675)       0.281
  99        ( 0.028   0.954)       0.281
 100        ( 0.747   1.673)       0.281
 101        (-1.483  -0.557)       0.281
 102        (-1.160  -0.235)       0.281
 103        (-0.661   0.265)       0.281
 104        (-1.001  -0.075)       0.281
 105        (-0.180   0.745)       0.281
 106        (-1.263  -0.337)       0.281
 107        (-0.073   0.853)       0.281
 108        (-1.298  -0.372)       0.281
 109        (-1.442  -0.516)       0.281
 110        (-1.163  -0.237)       0.281
 111        (-0.423   0.503)       0.281
 112        (-0.897   0.028)       0.281
 113        (-0.367   0.559)       0.281
 114        (-0.765   0.161)       0.281
 115        ( 0.031   0.957)       0.281
 116        (-0.268   0.658)       0.281
 117        (-1.887  -0.961)       0.281
 118        (-1.021  -0.095)       0.281
 119        (-1.871  -0.945)       0.281
 120        ( 0.011   0.936)       0.281
 121        ( 0.100   1.026)       0.281
 122        (-0.191   0.735)       0.281
 123        (-0.974  -0.049)       0.281
 124        ( 0.227   1.152)       0.281
 125        (-1.173  -0.247)       0.281
 126        (-0.676   0.250)       0.281
 127        (-0.996  -0.070)       0.281
 128        (-1.117  -0.192)       0.281
 129        (-1.368  -0.443)       0.281
 130        ( 0.180   1.106)       0.281
 131        (-0.288   0.638)       0.281
 132        (-0.888   0.037)       0.281
 133        ( 0.336   1.262)       0.281
 134        (-0.733   0.193)       0.281
 135        (-0.029   0.897)       0.281
 136        (-0.443   0.483)       0.281
 137        (-1.239  -0.314)       0.281
 138        (-0.540   0.386)       0.281
 139        ( 0.232   1.158)       0.281
 140        (-0.917   0.008)       0.281
 141        ( 0.603   1.528)       0.281
 142        (-0.675   0.251)       0.281
 143        (-0.658   0.267)       0.281
 144        (-1.475  -0.549)       0.281
 145        (-1.014  -0.088)       0.281
 146        ( 0.135   1.060)       0.281
 147        (-1.390  -0.464)       0.281
 148        (-1.257  -0.331)       0.281
 149        (-0.708   0.217)       0.281
 150        (-1.230  -0.304)       0.281
 151        (-0.208   0.718)       0.281
 152        (-0.871   0.054)       0.281
 153        (-0.913   0.013)       0.281
 154        (-1.574  -0.648)       0.281
 155        (-1.082  -0.156)       0.281
 156        (-0.618   0.308)       0.281
 157        (-1.501  -0.575)       0.281
 158        (-1.429  -0.504)       0.281
 159        (-1.132  -0.206)       0.281
 160        ( 0.293   1.218)       0.281
 161        ( 0.660   1.586)       0.281
 162        (-0.583   0.342)       0.281
 163        (-1.454  -0.529)       0.281
 164        ( 0.256   1.182)       0.281
 165        (-1.245  -0.319)       0.281
 166        ( 0.251   1.177)       0.281
 167        (-1.232  -0.307)       0.281
 168        (-1.020  -0.094)       0.281
 169        (-1.553  -0.627)       0.281
 170        (-1.270  -0.345)       0.281
 171        ( 0.617   1.543)       0.281
 172        (-0.734   0.192)       0.281
 173        (-1.473  -0.547)       0.281
 174        (-0.373   0.553)       0.281
 175        (-0.044   0.882)       0.281
 176        (-1.588  -0.662)       0.281
 177        (-1.387  -0.462)       0.281
 178        (-0.689   0.237)       0.281
 179        (-1.504  -0.578)       0.281
 180        (-1.683  -0.757)       0.281
 181        (-0.464   0.462)       0.281
 182        ( 1.015   1.940)       0.281
 183        (-1.109  -0.183)       0.281
 184        ( 0.200   1.125)       0.281
 185        (-1.309  -0.383)       0.281
 186        (-1.263  -0.337)       0.281
 187        (-0.889   0.037)       0.281
 188        (-0.838   0.087)       0.281
 189        (-1.420  -0.494)       0.281
 190        (-1.638  -0.712)       0.281
 191        ( 0.029   0.955)       0.281
 192        (-1.864  -0.938)       0.281
 193        (-1.537  -0.611)       0.281
 194        (-1.167  -0.241)       0.281
 195        (-1.651  -0.725)       0.281
 196        ( 0.303   1.228)       0.281
 197        (-0.700   0.226)       0.281
 198        (-1.232  -0.306)       0.281
 199        (-0.532   0.394)       0.281
 200        (-1.399  -0.473)       0.281
 201        (-1.491  -0.566)       0.281
 202        (-0.317   0.609)       0.281
 203        (-1.106  -0.181)       0.281
 204        ( 0.912   1.838)       0.281
 205        (-1.694  -0.768)       0.281
 206        (-1.331  -0.406)       0.281
 207        ( 0.326   1.252)       0.281
 208        (-1.034  -0.108)       0.281
 209        (-1.436  -0.510)       0.281
 210        (-1.771  -0.845)       0.281
 211        (-0.434   0.491)       0.281
 212        (-1.792  -0.866)       0.281
 213        (-1.792  -0.866)       0.281
 214        (-0.794   0.132)       0.281
 215        (-1.015  -0.089)       0.281
 216        (-1.643  -0.717)       0.281
 217        (-0.759   0.167)       0.281
 218        (-0.931  -0.006)       0.281
 219        ( 0.460   1.386)       0.281
 220        (-0.784   0.142)       0.281
 221        (-0.463   0.463)       0.281
 222        (-0.136   0.790)       0.281
 223        (-0.868   0.058)       0.281
 224        (-0.129   0.797)       0.281
 225        ( 0.891   1.816)       0.281
 226        (-1.192  -0.266)       0.281
 227        (-0.599   0.326)       0.281
 228        (-0.949  -0.023)       0.281
 229        (-0.167   0.759)       0.281
 230        ( 0.589   1.515)       0.281
 231        (-0.384   0.542)       0.281
 232        ( 0.293   1.218)       0.281
 233        (-1.430  -0.505)       0.281
 234        (-1.815  -0.889)       0.281
 235        (-1.815  -0.889)       0.281
 236        ( 0.341   1.266)       0.281
 237        ( 0.970   1.896)       0.281
 238        (-1.609  -0.683)       0.281
 239        (-0.030   0.896)       0.281
 240        ( 0.581   1.507)       0.281
 241        (-1.253  -0.327)       0.281
 242        (-1.161  -0.236)       0.281
 243        ( 0.022   0.948)       0.281
 244        ( 0.399   1.325)       0.281
 245        (-0.672   0.254)       0.281
 246        ( 0.159   1.084)       0.281
 247        (-1.724  -0.798)       0.281
 248        (-1.724  -0.798)       0.281
 249        (-0.355   0.571)       0.281
 250        (-0.582   0.344)       0.281
 251        ( 1.439   2.365)       0.281
 252        (-0.377   0.549)       0.281
 253        (-1.505  -0.580)       0.281
 254        (-1.694  -0.768)       0.281
 255        (-0.392   0.533)       0.281
 256        (-0.247   0.679)       0.281
 257        (-0.456   0.469)       0.281
 258        (-1.328  -0.403)       0.281
 259        ( 0.643   1.569)       0.281
 260        (-1.361  -0.435)       0.281
 261        (-1.912  -0.986)       0.281
 262        ( 0.925   1.851)       0.281
 263        ( 0.445   1.371)       0.281
 264        (-0.772   0.153)       0.281
 265        (-0.698   0.228)       0.281
 266        (-0.538   0.388)       0.281
 267        (-0.654   0.272)       0.281
 268        (-1.398  -0.472)       0.281
 269        ( 0.764   1.690)       0.281
 270        ( 0.134   1.060)       0.281
 271        (-1.652  -0.726)       0.281
 272        (-1.181  -0.255)       0.281
 273        ( 0.512   1.438)       0.281
 274        ( 1.861   2.787)       0.281
 275        ( 0.759   1.685)       0.281
 276        (-0.623   0.302)       0.281
 277        ( 0.441   1.367)       0.281
 278        (-1.205  -0.280)       0.281
 279        ( 0.504   1.430)       0.281
 280        ( 0.666   1.592)       0.281
 281        ( 0.598   1.524)       0.281
 282        (-0.939  -0.013)       0.281
 283        (-1.550  -0.624)       0.281
 284        ( 1.352   2.277)       0.281
 285        (-0.163   0.762)       0.281
 286        ( 0.375   1.300)       0.281
 287        (-2.067  -1.141)       0.281
 288        (-1.852  -0.926)       0.281
 289        (-1.161  -0.235)       0.281
 290        (-0.353   0.573)       0.281
 291        ( 1.634   2.560)       0.281
 292        (-1.452  -0.526)       0.281
 293        (-0.204   0.722)       0.281
 294        (-0.898   0.028)       0.281
 295        ( 0.875   1.801)       0.281
 296        (-1.719  -0.793)       0.281
 297        (-2.159  -1.233)       0.281
 298        (-1.401  -0.476)       0.281
 299        ( 0.921   1.847)       0.281
 300        (-1.385  -0.459)       0.281
 301        ( 0.899   1.824)       0.281
 302        (-1.119  -0.193)       0.281
 303        ( 0.382   1.308)       0.281
 304        (-0.786   0.139)       0.281
 305        ( 2.298   3.223)       0.281
 306        ( 2.980   3.905)       0.281
 307        ( 1.453   2.379)       0.281
 308        (-1.571  -0.645)       0.281
 309        (-1.336  -0.410)       0.281
 310        (-1.288  -0.362)       0.281
 311        ( 1.865   2.791)       0.281
 312        ( 0.230   1.156)       0.281
 313        ( 1.760   2.686)       0.281
 314        (-0.398   0.528)       0.281
 315        (-1.309  -0.383)       0.281
 316        (-1.425  -0.499)       0.281
 317        ( 1.581   2.507)       0.281
 318        (-0.210   0.715)       0.281
 319        ( 1.309   2.235)       0.281
 320        (-0.480   0.446)       0.281
 321        ( 2.755   3.680)       0.281
 322        (-0.928  -0.003)       0.281
 323        (-0.406   0.520)       0.281
 324        ( 0.982   1.907)       0.281
 325        (-0.107   0.819)       0.281
 326        ( 0.893   1.819)       0.281
 327        (-0.171   0.755)       0.281
 328        (-0.994  -0.069)       0.281
 329        (-0.576   0.350)       0.281
 330        (-2.063  -1.137)       0.281
 331        (-1.513  -0.588)       0.281
 332        (-0.335   0.590)       0.281
 333        (-0.083   0.843)       0.281
 334        (-1.017  -0.091)       0.281
 335        ( 0.649   1.574)       0.281
 336        ( 1.920   2.846)       0.281
 337        ( 1.023   1.949)       0.281
 338        ( 0.320   1.246)       0.281
 339        ( 3.510   4.435)       0.281
 340        ( 1.356   2.282)       0.281
 341        ( 0.205   1.130)       0.281
 342        (-0.094   0.831)       0.281
 343        ( 0.591   1.517)       0.281
 344        ( 0.777   1.703)       0.281
 345        ( 1.955   2.880)       0.281
 346        (-0.334   0.592)       0.281
 347        (-0.259   0.667)       0.281
 348        ( 3.365   4.290)       0.281


ACCURACY OF FACTOR SCORE ESTIMATES 

FACTOR:  2 

Case         Approximate 95%       Posterior   
             confidence interval   SE         

   1        (-0.342   0.703)       0.318
   2        (-0.065   0.980)       0.318
   3        (-1.104  -0.059)       0.318
   4        (-0.461   0.584)       0.318
   5        (-0.177   0.868)       0.318
   6        ( 0.001   1.046)       0.318
   7        (-0.130   0.915)       0.318
   8        ( 0.046   1.091)       0.318
   9        (-1.006   0.039)       0.318
  10        (-1.034   0.011)       0.318
  11        (-0.728   0.317)       0.318
  12        (-0.391   0.654)       0.318
  13        (-0.074   0.971)       0.318
  14        (-0.502   0.544)       0.318
  15        (-1.059  -0.014)       0.318
  16        (-0.404   0.641)       0.318
  17        (-0.831   0.214)       0.318
  18        (-0.136   0.909)       0.318
  19        (-0.009   1.036)       0.318
  20        (-0.252   0.793)       0.318
  21        (-0.860   0.185)       0.318
  22        (-0.751   0.294)       0.318
  23        (-1.865  -0.820)       0.318
  24        (-0.625   0.420)       0.318
  25        (-0.555   0.490)       0.318
  26        (-0.647   0.398)       0.318
  27        (-0.895   0.150)       0.318
  28        (-1.266  -0.221)       0.318
  29        ( 0.018   1.063)       0.318
  30        ( 0.201   1.246)       0.318
  31        (-0.557   0.488)       0.318
  32        (-0.799   0.246)       0.318
  33        (-0.416   0.629)       0.318
  34        ( 0.006   1.052)       0.318
  35        (-0.179   0.866)       0.318
  36        (-0.601   0.444)       0.318
  37        (-0.599   0.446)       0.318
  38        (-0.943   0.102)       0.318
  39        (-1.094  -0.049)       0.318
  40        (-0.037   1.008)       0.318
  41        ( 0.336   1.381)       0.318
  42        (-0.374   0.671)       0.318
  43        (-0.810   0.235)       0.318
  44        (-0.543   0.502)       0.318
  45        (-0.119   0.926)       0.318
  46        (-0.111   0.934)       0.318
  47        (-0.625   0.420)       0.318
  48        (-0.181   0.864)       0.318
  49        (-0.134   0.911)       0.318
  50        ( 0.530   1.575)       0.318
  51        (-0.781   0.264)       0.318
  52        (-0.271   0.774)       0.318
  53        (-0.307   0.738)       0.318
  54        (-0.556   0.489)       0.318
  55        (-0.721   0.324)       0.318
  56        (-1.276  -0.231)       0.318
  57        (-1.076  -0.030)       0.318
  58        ( 0.123   1.168)       0.318
  59        (-0.232   0.813)       0.318
  60        (-0.740   0.305)       0.318
  61        (-0.655   0.390)       0.318
  62        (-0.735   0.311)       0.318
  63        (-0.572   0.473)       0.318
  64        ( 0.524   1.569)       0.318
  65        (-0.515   0.530)       0.318
  66        (-0.685   0.361)       0.318
  67        ( 0.313   1.358)       0.318
  68        (-0.320   0.725)       0.318
  69        (-1.860  -0.815)       0.318
  70        (-0.582   0.463)       0.318
  71        (-0.978   0.067)       0.318
  72        (-0.680   0.365)       0.318
  73        (-0.751   0.294)       0.318
  74        (-0.056   0.989)       0.318
  75        (-0.731   0.314)       0.318
  76        (-1.137  -0.092)       0.318
  77        (-0.871   0.174)       0.318
  78        (-0.274   0.771)       0.318
  79        (-0.698   0.347)       0.318
  80        (-1.167  -0.122)       0.318
  81        (-0.735   0.310)       0.318
  82        (-0.511   0.534)       0.318
  83        (-0.511   0.534)       0.318
  84        (-0.301   0.744)       0.318
  85        (-0.328   0.717)       0.318
  86        (-0.095   0.950)       0.318
  87        (-0.168   0.877)       0.318
  88        (-0.664   0.381)       0.318
  89        (-0.325   0.720)       0.318
  90        (-1.117  -0.072)       0.318
  91        (-0.820   0.225)       0.318
  92        (-0.314   0.731)       0.318
  93        (-0.717   0.328)       0.318
  94        (-1.641  -0.596)       0.318
  95        (-0.794   0.251)       0.318
  96        ( 0.023   1.068)       0.318
  97        (-0.797   0.248)       0.318
  98        (-0.570   0.475)       0.318
  99        (-0.684   0.361)       0.318
 100        (-1.086  -0.040)       0.318
 101        (-0.071   0.974)       0.318
 102        (-0.341   0.704)       0.318
 103        (-0.254   0.791)       0.318
 104        ( 0.839   1.884)       0.318
 105        (-0.834   0.211)       0.318
 106        (-0.771   0.274)       0.318
 107        (-1.191  -0.146)       0.318
 108        (-1.028   0.017)       0.318
 109        ( 0.594   1.639)       0.318
 110        ( 0.209   1.254)       0.318
 111        (-0.673   0.372)       0.318
 112        (-0.541   0.504)       0.318
 113        ( 0.195   1.240)       0.318
 114        (-0.505   0.540)       0.318
 115        (-0.848   0.197)       0.318
 116        ( 0.453   1.498)       0.318
 117        (-0.091   0.955)       0.318
 118        ( 0.275   1.320)       0.318
 119        ( 0.132   1.177)       0.318
 120        (-1.238  -0.193)       0.318
 121        ( 0.292   1.337)       0.318
 122        ( 0.369   1.414)       0.318
 123        ( 0.324   1.369)       0.318
 124        (-1.900  -0.855)       0.318
 125        (-0.117   0.928)       0.318
 126        ( 0.006   1.051)       0.318
 127        ( 0.112   1.157)       0.318
 128        (-0.196   0.849)       0.318
 129        (-0.640   0.405)       0.318
 130        ( 0.131   1.176)       0.318
 131        (-0.631   0.414)       0.318
 132        (-0.038   1.007)       0.318
 133        (-1.182  -0.137)       0.318
 134        (-0.757   0.288)       0.318
 135        (-0.714   0.331)       0.318
 136        ( 0.607   1.652)       0.318
 137        (-0.213   0.832)       0.318
 138        ( 0.113   1.158)       0.318
 139        (-0.796   0.249)       0.318
 140        (-1.029   0.016)       0.318
 141        (-2.483  -1.438)       0.318
 142        ( 0.175   1.220)       0.318
 143        (-1.552  -0.507)       0.318
 144        ( 0.468   1.513)       0.318
 145        (-0.477   0.568)       0.318
 146        (-0.800   0.246)       0.318
 147        (-0.420   0.625)       0.318
 148        ( 0.113   1.158)       0.318
 149        ( 0.369   1.414)       0.318
 150        ( 0.336   1.381)       0.318
 151        ( 0.005   1.050)       0.318
 152        (-0.583   0.462)       0.318
 153        ( 0.731   1.776)       0.318
 154        ( 0.438   1.483)       0.318
 155        ( 0.080   1.125)       0.318
 156        (-0.479   0.566)       0.318
 157        ( 0.371   1.416)       0.318
 158        ( 0.383   1.428)       0.318
 159        ( 0.142   1.187)       0.318
 160        (-0.918   0.127)       0.318
 161        (-0.650   0.395)       0.318
 162        (-0.423   0.622)       0.318
 163        (-0.561   0.484)       0.318
 164        (-0.805   0.240)       0.318
 165        ( 0.210   1.255)       0.318
 166        (-0.870   0.175)       0.318
 167        ( 0.094   1.139)       0.318
 168        ( 0.171   1.216)       0.318
 169        (-1.198  -0.153)       0.318
 170        ( 0.356   1.401)       0.318
 171        (-0.861   0.184)       0.318
 172        (-0.916   0.129)       0.318
 173        ( 0.645   1.690)       0.318
 174        (-0.126   0.919)       0.318
 175        (-0.795   0.250)       0.318
 176        ( 0.653   1.698)       0.318
 177        ( 0.517   1.562)       0.318
 178        (-0.519   0.526)       0.318
 179        ( 0.547   1.592)       0.318
 180        ( 0.429   1.474)       0.318
 181        ( 0.287   1.332)       0.318
 182        (-0.060   0.985)       0.318
 183        ( 0.321   1.366)       0.318
 184        (-1.031   0.014)       0.318
 185        ( 0.479   1.524)       0.318
 186        (-0.202   0.843)       0.318
 187        (-0.093   0.952)       0.318
 188        ( 0.737   1.782)       0.318
 189        ( 0.113   1.158)       0.318
 190        ( 0.674   1.719)       0.318
 191        (-0.439   0.606)       0.318
 192        ( 0.644   1.690)       0.318
 193        ( 0.531   1.576)       0.318
 194        ( 0.029   1.074)       0.318
 195        ( 0.651   1.696)       0.318
 196        (-1.375  -0.330)       0.318
 197        ( 0.242   1.287)       0.318
 198        ( 0.323   1.368)       0.318
 199        (-0.479   0.566)       0.318
 200        ( 0.547   1.592)       0.318
 201        ( 0.596   1.641)       0.318
 202        (-0.419   0.627)       0.318
 203        ( 0.353   1.398)       0.318
 204        (-2.397  -1.352)       0.318
 205        ( 0.452   1.497)       0.318
 206        (-0.167   0.878)       0.318
 207        (-0.305   0.740)       0.318
 208        ( 0.019   1.064)       0.318
 209        ( 0.512   1.557)       0.318
 210        ( 0.513   1.558)       0.318
 211        (-1.011   0.034)       0.318
 212        ( 0.624   1.669)       0.318
 213        ( 0.624   1.669)       0.318
 214        ( 1.163   2.208)       0.318
 215        ( 0.247   1.292)       0.318
 216        (-0.884   0.161)       0.318
 217        (-0.192   0.853)       0.318
 218        (-0.001   1.044)       0.318
 219        (-1.677  -0.632)       0.318
 220        (-1.165  -0.120)       0.318
 221        ( 0.549   1.594)       0.318
 222        (-0.337   0.708)       0.318
 223        ( 0.231   1.276)       0.318
 224        (-1.096  -0.051)       0.318
 225        (-1.842  -0.797)       0.318
 226        ( 0.753   1.798)       0.318
 227        (-0.312   0.733)       0.318
 228        ( 0.640   1.685)       0.318
 229        (-0.519   0.526)       0.318
 230        (-3.553  -2.508)       0.318
 231        (-2.064  -1.019)       0.318
 232        (-1.495  -0.450)       0.318
 233        ( 1.796   2.841)       0.318
 234        ( 0.592   1.637)       0.318
 235        ( 0.592   1.637)       0.318
 236        (-0.440   0.605)       0.318
 237        (-1.268  -0.223)       0.318
 238        (-0.106   0.939)       0.318
 239        (-0.044   1.001)       0.318
 240        (-0.774   0.271)       0.318
 241        ( 0.331   1.376)       0.318
 242        ( 0.052   1.097)       0.318
 243        (-1.580  -0.535)       0.318
 244        (-0.422   0.623)       0.318
 245        ( 0.232   1.277)       0.318
 246        ( 0.215   1.260)       0.318
 247        ( 0.723   1.768)       0.318
 248        ( 0.723   1.768)       0.318
 249        ( 0.154   1.199)       0.318
 250        (-0.818   0.227)       0.318
 251        (-0.674   0.372)       0.318
 252        ( 0.429   1.474)       0.318
 253        (-0.376   0.669)       0.318
 254        (-0.385   0.661)       0.318
 255        (-0.502   0.543)       0.318
 256        ( 0.236   1.281)       0.318
 257        ( 0.855   1.900)       0.318
 258        (-0.526   0.520)       0.318
 259        (-4.569  -3.524)       0.318
 260        ( 0.715   1.760)       0.318
 261        (-0.382   0.663)       0.318
 262        ( 0.577   1.622)       0.318
 263        (-1.101  -0.056)       0.318
 264        (-0.040   1.005)       0.318
 265        (-0.252   0.793)       0.318
 266        (-1.475  -0.430)       0.318
 267        (-0.354   0.691)       0.318
 268        ( 0.078   1.123)       0.318
 269        (-1.073  -0.028)       0.318
 270        (-0.764   0.281)       0.318
 271        (-0.088   0.957)       0.318
 272        (-0.645   0.400)       0.318
 273        (-1.103  -0.058)       0.318
 274        (-3.805  -2.760)       0.318
 275        (-0.693   0.352)       0.318
 276        (-0.716   0.329)       0.318
 277        (-1.227  -0.182)       0.318
 278        (-0.293   0.752)       0.318
 279        (-0.740   0.305)       0.318
 280        (-1.362  -0.317)       0.318
 281        ( 0.426   1.471)       0.318
 282        (-0.120   0.925)       0.318
 283        (-1.000   0.045)       0.318
 284        (-1.569  -0.524)       0.318
 285        (-0.887   0.158)       0.318
 286        (-1.008   0.037)       0.318
 287        (-0.265   0.780)       0.318
 288        (-1.485  -0.440)       0.318
 289        (-0.226   0.820)       0.318
 290        (-0.801   0.244)       0.318
 291        (-4.366  -3.321)       0.318
 292        (-0.593   0.452)       0.318
 293        ( 0.353   1.398)       0.318
 294        (-0.459   0.586)       0.318
 295        (-1.592  -0.547)       0.318
 296        (-0.272   0.773)       0.318
 297        (-0.707   0.338)       0.318
 298        ( 0.029   1.074)       0.318
 299        (-0.326   0.719)       0.318
 300        (-0.145   0.900)       0.318
 301        (-1.195  -0.150)       0.318
 302        ( 0.535   1.580)       0.318
 303        (-0.383   0.662)       0.318
 304        (-1.132  -0.087)       0.318
 305        (-3.481  -2.436)       0.318
 306        (-3.531  -2.486)       0.318
 307        (-0.282   0.763)       0.318
 308        ( 0.602   1.647)       0.318
 309        ( 0.957   2.002)       0.318
 310        (-0.597   0.448)       0.318
 311        (-1.139  -0.094)       0.318
 312        (-1.619  -0.574)       0.318
 313        (-1.869  -0.824)       0.318
 314        (-0.684   0.361)       0.318
 315        (-1.472  -0.427)       0.318
 316        (-0.146   0.899)       0.318
 317        (-0.910   0.135)       0.318
 318        (-0.714   0.331)       0.318
 319        (-1.423  -0.378)       0.318
 320        ( 0.837   1.882)       0.318
 321        (-4.019  -2.974)       0.318
 322        (-1.125  -0.080)       0.318
 323        (-2.125  -1.080)       0.318
 324        (-2.066  -1.021)       0.318
 325        ( 0.311   1.356)       0.318
 326        (-0.285   0.760)       0.318
 327        (-0.594   0.451)       0.318
 328        (-0.796   0.249)       0.318
 329        (-0.651   0.394)       0.318
 330        ( 0.193   1.238)       0.318
 331        ( 0.778   1.823)       0.318
 332        (-1.093  -0.048)       0.318
 333        (-0.575   0.470)       0.318
 334        (-0.938   0.107)       0.318
 335        (-2.552  -1.507)       0.318
 336        (-1.483  -0.437)       0.318
 337        (-2.673  -1.628)       0.318
 338        (-0.055   0.990)       0.318
 339        (-4.980  -3.935)       0.318
 340        (-1.732  -0.687)       0.318
 341        (-0.949   0.096)       0.318
 342        (-2.023  -0.978)       0.318
 343        (-2.889  -1.844)       0.318
 344        (-2.302  -1.257)       0.318
 345        (-4.314  -3.269)       0.318
 346        ( 0.121   1.166)       0.318
 347        (-0.858   0.187)       0.318
 348        (-5.218  -4.173)       0.318




--------------------------------------------------------------------------------

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Timmerman, M. E., & Lorenzo-Seva, U. (2011). Dimensionality Assessment of Ordered Polytomous Items with Parallel Analysis. Psychological Methods, 16, 209-220. doi:10.1037/a0023353

Woodhouse, B. & Jackson, P.H. (1977). Lower bounds to the reliability of the total score on a test composed of nonhomogeneous items: II. A search procedure to locate the greatest lower bound. Psychometrika, 42, 579-591. doi:10.1007/bf02295980

Wu, A.D., & Zumbo, B.D. (2017). Using Pratt's Importance Measures in Confirmatory Factor Analyses. Journal of Modern Applied Statistical Methods, 16(2), 81-98. doi:10.22237/jmasm/1509494700

Yu, C., & Muthen, B. (2002, April). Evaluation of model fit indices for latent variable models with categorical and continuous outcomes. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, L.A.

       

FACTOR is based on CLAPACK.
 Anderson, E., Bai, Z., Bischof, C., Blackford, S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A., & Sorensen, D. (1999). LAPACK Users' Guide. Society for Industrial and Applied Mathematics. Philadelphia, PA

FACTOR can be refered as:
 Ferrando, P.J., & Lorenzo-Seva, U. (2017). Program FACTOR at 10: origins, development and future directions. Psicothema, 29(2), 236-241. doi: 10.7334/psicothema2016.304
 Lorenzo-Seva, U., & Ferrando, P.J. (2013). FACTOR 9.2 A comprehensive program for fitting exploratory and semiconfirmatory factor analysis and IRT models. Applied Psychological Measurement, 37(6), 497-498. doi:10.1177/0146621613487794
 Lorenzo-Seva, U., & Ferrando, P.J. (2006). FACTOR: A computer program to fit the exploratory factor analysis model. Behavioral Research Methods, 38(1), 88-91. 10.3758/bf03192753

For further information and new releases go to:
psico.fcep.urv.cat/utilitats/factor 
www.psicologia.urv.cat/en/tools/ 

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FACTOR completed

Computing time     :  6.93 minutes.
Matrices generated : 20314871 

Our last advice: Distrust 5% of statistics, and 95% of statisticians. (Cal desconfiar un 5% de l'estadistica, i un 95% de l'estadistic.)

