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. 2023 Sep 1;18(9):e0290728. doi: 10.1371/journal.pone.0290728

Table 1. Factor characteristics based on different factor extraction and factor rotation techniques.

Factor Extraction method
Dataset		 Factor Rotation Technique
No rotation Varimax Equamax Quartimax
F1 F2 F3 F1 F2 F3 F1 F2 F3 F1 F2 F3

Principal Component 		Dataset 1 (40 Q-sorts):
loaded Q-sorts
dist. Statements
changed statements+
22
5
-
6
7
-
2
10
-
13
8
5 (-2)
8
5
2 (-4)
10
3
2 (-4)1
10
7
2 (-5)3
10
2
1 (-6)
9
6
2 (-3)2
20
8
6 (-3)
8
6
4 (-5)
5
5
3 (-8)
Dataset 2 (33 Q-sorts):
loaded Q-sorts
dist. Statements
changed statements+
29
8
-
1
15
-
3
7
-
17
5
1 (-4)
10
6
3 (-12)
5
8
3 (-2)
10
7
6 (-14)2
6
7
3 (-3)3
9
7
3 (-11)2
28
7
1 (-2)
2
9
4 (-2)3
1
16
3 (-2)2

Principal Axis Factoring 		Dataset 1 (40 Q-sorts):
loaded Q-sorts
dist. Statements
changed statements+
21
5
-
6
9
-
1
8
-
11
8
6 (-3)
8
6
3 (-6)
8
4
3 (-7)
10
5
3 (-6)3
10
3
2 (-8)
7
5
2 (-6)2
19
6
4 (-3)
7
5
2 (-6)
4
5
2 (-5)

+ Number of changed statements includes both the new statements (positive number) and the statements that did not reappear in the new rotation (negative numbers) compared to no-rotation.

1 Matched with Factor 1 of no-rotation.

2 Matched with Factor 2 of no-rotation.

3 Matched with Factor 3 of no-rotation.