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Table 4 Rotated factor matrixa in PCA

From: Virtual classes during COVID-19 pandemic: focus on university students’ affection, perceptions, and problems in the light of resiliency and self-image

Items

Component

1

2

3

4

5

Item35

− .915

    

Item34

− .885

    

Item28

− .885

    

Item37

− .880

    

Item36

− .875

    

Item26

.871

    

Item24

.852

    

Item25

.846

    

Item17

.812

    

Item27

.771

    

Item12

 

.857

   

Item10

 

.823

   

Item13

 

.819

   

Item7

 

.673

   

Item5

 

.669

   

Item9

 

.491

 

.423

 

Item11

 

.489

 

.397

 

Item6

 

.487

 

.346

 

Item8

 

.448

 

.443

 

Item30

  

.891

  

Item33

  

.839

  

Item29

  

.819

  

Item33

  

.593

  

Item31

  

.523

  

Item4

   

.855

 

Item16

   

.835

 

Item3

   

.832

 

Item1

   

.782

 

Item15

   

.772

 

Item2

   

.733

 

Item14

   

.520

 

Item21

    

.873

Item22

    

.729

Item23

    

.670

Item18

    

.599

Item20

    

.544

Item19

    

.487

  1. Extraction method: principal component analysis
  2. Rotation Method: Oblimin with Kaiser Normalizationa
  3. aRotation converged in 17 iterations