14
.653 | 1,519 | 74,283 | ||||
15 | .623 | 1,450 | 75,732 | |||
16 | .598 | 1,390 | 77,122 | |||
17 | .579 | 1,347 | 78,469 | |||
18 | .532 | 1,236 | 79,705 | |||
19 | .525 | 1,222 | 80,927 | |||
20 | .520 | 1,209 | 82,137 | |||
21 | .506 | 1,177 | 83,313 | |||
22 | .486 | 1.129 | 84,443 | |||
23 | .472 | 1,097 | 85,540 | |||
24 | .444 | 1,032 | 86,572 | |||
25 | .432 | 1,004 | 87,575 | |||
26 | .418 | .971 | 88,546 | |||
27 | .405 | .942 | 89,488 | |||
28 | .399 | .927 | 90,415 | |||
29 | .381 | .885 | 91,300 | |||
30 | .350 | .815 | 92,115 | |||
31 | .348 | .808 | 92,923 | |||
32 | .327 | .760 | 93,683 | |||
33 | .320 | .744 | 94,428 | |||
34 | .309 | .718 | 95,146 | |||
35 | .289 | .672 | 95,818 | |||
36 | .266 | .619 | 96,436 | |||
37 | .257 | .598 | 97,034 |
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38
.252 | .586 | 97,621 | ||||
39 | .235 | .547 | 98,168 | |||
40 | .218 | .506 | 98,674 | |||
41 | .211 | .492 | 99,165 | |||
42 | .186 | .433 | 99,599 | |||
43 | .173 | .401 | 100,000 |
Extraction Method: Principal Axis Factoring.
a. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.
Pattern Matrix
Factor | |||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
HQ7 | .739 | ||||||||
HQ6 | .712 | ||||||||
HQ8 | .700 | ||||||||
HQ1 | .700 | ||||||||
HQ5 | .700 | ||||||||
HQ2 | .693 | ||||||||
HQ4 | .623 | ||||||||
HQ3 | .587 | ||||||||
DBRR4 | .792 | ||||||||
DBRR1 | .777 | ||||||||
DBRR6 | .761 | ||||||||
DBRR2 | .707 | ||||||||
DBRR5
.607 | ||||||||
DBRR3 | .587 | |||||||
VM1 | .863 | |||||||
VM4 | .821 | |||||||
VM2 | .803 | |||||||
VM3 | .704 | |||||||
VM5 | .617 | |||||||
QTRR2 | .773 | |||||||
QTRR3 | .721 | |||||||
QTRR5 | .698 | |||||||
QTRR1 | .693 | |||||||
QTRR4 | .642 | |||||||
KTGS4 | .808 | |||||||
KTGS3 | .798 | |||||||
KTGS1 | .791 | |||||||
KTGS2 | .784 | |||||||
IT3 | .825 | |||||||
IT1 | .809 | |||||||
IT4 | .807 | |||||||
IT2 | .735 | |||||||
KD3 | .860 | |||||||
KD2 | .782 | |||||||
KD4 | .767 | |||||||
KD1 | .758 |
LS4
.784
LS1
.765
LS3
.750
LS2
.686
NLNH1
.823
NLNH3
.764
NLNH2
.682
Extraction Method: Principal Axis Factoring.
Rotation Method: Promax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
Factor Correlation Matrix
Factor
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
1 | 1,000 | .045 | .047 | .016 | .071 | .021 | .038 | .104 | .063 |
2 | .045 | 1,000 | .390 | .408 | .290 | .298 | .137 | .283 | .276 |
3 | .047 | .390 | 1,000 | .127 | .150 | .088 | .116 | .169 | .043 |
4 | .016 | .408 | .127 | 1,000 | .086 | .110 | .038 | .115 | .134 |
5 | .071 | .290 | .150 | .086 | 1,000 | .043 | .060 | .070 | .103 |
6 | .021 | .298 | .088 | .110 | .043 | 1,000 | .023 | .038 | .110 |
7 | .038 | .137 | .116 | .038 | .060 | .023 | 1,000 | .105 | .008 |
8 | .104 | .283 | .169 | .115 | .070 | .038 | .105 | 1,000 | .123 |
9 | .063 | .276 | .043 | .134 | .103 | .110 | .008 | .123 | 1,000 |
Extraction Method: Principal Axis Factoring.
Rotation Method: Promax with Kaiser Normalization.
CFA

SEM






