Open Access e-Journal Cardiometry No.16 May 2020

Table 2
Eigenvalues and percentage of an interpretable dispersion of factors after Varimax rotation
Factor: 1 2 3 4 5 6 7 8
Eigenvalue 1,02 1,009 0,9981 1,015 1,021 0,9956 0,9721 0,9687
Dispersion (%) 12,75 12,61 12,48 12,69 12,77 12,45 12,15 12,11
Cumulative % 12,75 25,36 37,84 59,53 63,29 75,74 87,89 100
Table 3
Eigenvalues and percentage of an interpretable dispersion of
factors after Varimax rotation at the stage of the confirmatory
factor analysis
Factor: 1 2 3 4
Eigenvalue 1,02 1,009 0,9981 1,015
Dispersion (%) 12,75 12,61 12,48 12,69
Cumulative % 12,75 25,36 37,84 59,53
Table 4
Factor structure of the correlation relationships after Varimax
rotation at the stage of the confirmatory factor analysis
Ego-states
Number of factor
1 2 3 4
1 -0,806
2 -0,7626
3 -0,775
4 -0,756
5 0,6129 -0,5775
6 0,7088
7 0,7995
8 0,95
ric closeness to each individual factor, has been completed.
In addition to the orthogonal rotation method
(Varimax rotation), applied have been the Quartimax
rotation, Equimax rotation and Oblique rotation
methods.
The tables given below illustrate the results of various
variants for optimizing the factor structure at the
stage of the confirmatory factor analysis. The indicated
data are obtained by processing the matrix of the
Spearman rank correlation coefficients. Tables 4, 6,
and 8, according to the recommendations in paper [4],
give values of factor loading not lower than 0.5 only.
Tables 3 and 4 show the parameters of the factor
structure after using the orthogonal rotation method
(Varimax rotation), with which we sought to minimize
the number of variables with high loadings on
each factor.
Tables 5 and 6 herein present the data obtained
after applying the Quartimax rotation method, with
which we tried to minimize the number of factors, required
for a meaningful interpretation of each of the
variables used.
Tables 7 and 8 given herein indicate the results
obtained from the Equimax rotation method, which
was used to simultaneously minimize the number of
Table 5
Eigenvalues and percentage of an interpretable dispersion
of factors after Quartimax rotation at the stage of the
confirmatory factor analysis
Factor: 1 2 3 4
Eigenvalue 1,85 1, 935 1,324 0,8993
Dispersion (%) 23,13 24,19 16,55 11,24
Cumulative % 23,13 47,32 63,86 75,1
Table 6
Factor structure of the correlation relationships after
Quartimax rotation at the stage of the confirmatory factor
analysis
Ego-states
Number of factor
1 2 3 4
1 -0,8259
2 -0,7325
3 -0,7874
4 -0,7305
5 0,6031 -0,5752
6 0,7308
7 0,8187
8 0,9271
variables with large factor loadings and the number of
factors interpreting them.
We have also completed the Oblique rotation procedure,
with which we have sought to minimize the
number of factors without providing their complete
independence (orthogonality). It has turned out that
the factor structure of the correlation relationships according
to the Oblique rotation method exactly corresponds
to that structure which has been obtained
upon the Varimax rotation application.
As shown in Tables 3, 5 and 7, the hypothesis for an
applicability of employing 4 factors for describing the
optimal structure of the latent relations, provided that
the analyzed objects with eigenvalues greater than 1
are referred to as the main components of the scattering
ellipse axes, is confirmed for the Equimax rotation
method only. Therefore, at the final stage of the confirmatory
factor analysis, all the above main components
calculation procedures and all the above types of their
rotation have been performed for models containing
only 3 factors. The results from the final stage of the
confirmatory factor analysis are presented in Tables
9-14 herein.
As it is the case with all the previous stages, in order
to identify the correlation relationships, we have
58 | Cardiometry | Issue 16. May 2020