Cjss second Issue: cjss second Issue qxd

Output 3. Modified table of statistics of the extracted components
Extraction Method: Principal Component Analysis
The initial total Eigenvalues include the characteristic features of the
correlation matrix and determine which factors stay in the matrix.  The fac-
tors Eigenvalues of less that 1 will be extracted.  The Eigenvalue is the number
of variance of the whole questionnaire determined by each factor, where the
total variance of each test is represented as 100%. The percentage of variance
supplies information about responsibility of concrete factor for the certain
percentage of variance. Thus, the first three factors are represented as having
more responsibility.  
Here is graphically represented the meaning of each extracted factor. The
variance conditioned by the factors sharply skewed with extraction of each of
them.  We are interested in which point the curve becomes a straight line. In
this case this is the space between the third and the fourth factors. Notably,
the fourth factor meaningfully is more importance than the first, so it would
be involved in factor analysis discussion.
22
Caucasus Journal of Social Sciences
Component
Initial Eigen values
Total
% of
Variance
Cumulative
%
1
6,026
33,480
33,480
2
4,233
23,515
56,995
3
2,553
14,182
71,177
4
1,155
6,415
77,593
5
0,953
5,296
82,889
6
0,755
4,197
87,086
7
0,676
3,754
90,840
8
0,434
2,410
93,250
9
0,395
2,193
95,443
10
0,260
1,445
96,888
11
0,194
1,075
97,963
12
0,154
0,854
98,817
13
0,109
0,606
99,423
14
0,048
0,265
99,689
15
0,030
0,166
99,855
16
0,018
0,099
99,954
17
0,008
0,046
100,000
18
0,000
0,000
100,000

---

Figure 1. Graph of the results of factor analysis
Output 4. The meanings of rested tests on four extracted factors. 
Component Matrix (a)
23
Caucasus Journal of Social Sciences
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18
Component Number
0
1
2
3
4
5
6
7
Eigenvalue
Scree Plot
Component
1
2
3
4
f1
-0,315
-0,342
0,463
0,370
f2
0,825
-0,188
-0,005
0,300
f3
0,789
-0,256
-0,110
0,442
f4
0,880
-0,170
0,107
-0,148
f5
0,457
0,097
0,669
-0,052
f6
0,669
-0,546
0,103
-0,134
f7
0,480
-0,648
-0,323
-0,227
f8
0,040
0,053
0,963
0,064
f9
0,734
0,034
0,444
-0,109
f10
0,754
-0,159
0,159
-0,372
f11
0,209
0,773
0,257
-0,389
f12
0,393
0,196
-0,637
-0,241
f13
0,749
-0,264
-0,214
0,339
f14
0,051
0,893
-0,049
0,026
f15
0,121
0,827
0,018
0,303
f16
0,605
0,568
-0,277
0,235
f17
0,779
0,470
-0,022
-0,070
f18
0,322
0,706
-0,064
0,084

---

Extraction Method: Principal Component Analysis. a 4 components ex-
tracted
Vertically placed factors which are not in correlation with each other
represent correlation coefficients between the variables and factors. Thus,
how much high is an absolute value of meaning (which cannot be more than
1), so much valuable is the contribution of factor in variance of points of the
variable. As it has been shown in the correlation matrix factor analysis, four
factors have been extracted. 
Output 7. The matrix of rotated components. 
Rotated Component Matrix(a)
Extraction Method: Principal Component Analysis; Rotation Method:
Varimax with Kaiser Normalization; Rotation converged in 9 iterations
The aim of rotation is to achieve a new position for an axis (factor) in
order to interpret more easily from a psychological point of view. This matrix
makes it possible to indicate which factors have high correlation (near to 1)
with the first four factors. Besides F2, F3 and F4 which were extracted, these
factors are: 
F8 - function and roles of the press antagonistic to the Communist press
F9 - function and role in reflecting social changes
F10 - function and role in calling for social changes
F11 - function and role in reminding about national identity
F12 - function and role in restricting writing with exaggerated 
nationalistic pathos
24
Caucasus Journal of Social Sciences
Component
1
2
3
4
f1
-0,290
0,024
-0,174
-0,673
f2
0,049
0,812
0,378
0,039
f3
0,006
0,918
0,224
0,045
f4
-0,020
0,536
0,721
0,175
f5
0,192
0,117
0,665
-0,418
f6
-0,417
0,507
0,580
0,088
f7
-0,586
0,433
0,295
0,435
f8
0,104
-0,141
0,476
-0,824
f9
0,166
0,334
0,768
-0,142
f10
-0,082
0,292
0,788
0,210
f11
0,700
-0,376
0,467
0,092
f12
0,189
0,189
0,068
0,762
f13
-0,037
0,848
0,202
0,171
f14
0,867
-0,192
-0,025
0,123
f15
0,881
0,039
-0,098
-0,055
f16
0,704
0,460
0,116
0,317
f17
0,584
0,361
0,531
0,281
f18
0,754
0,088
0,118
0,156

---