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You need to do interpretation these three tables in single page
Factor Analysis output
Table 1
Communalities
Initial
Extraction
GENDER
1.000
.726
AGE
.782
The level of knowledge from online learning is equal to the traditional learning.
.564
I believe that the online learning opens up new opportunities for me
.820
I am aware of the specific knowledge requirements for online learning
.796
Online learning runs smooth at all times.
.884
The queries arising during online learning gets resolved in due course.
.703
The overall nature of classes and the faculty is similar to any other mode of learning
.623
The level of concentration is not hurt during online learning.
.707
My teacher regularly communicates with me.
.744
The interpersonal interaction is not affected as a result of online classes.
.587
I like my online teaching schedule.
.749
I am able to upload assignments without any hassle.
.858
I plan the individual learning task online.
I like taking online tests
.841
I love presenting PPT online.
.676
The class timings are suitable for me.
.769
The overall mode of books and study materials helps me understand the concepts properly.
.529
The evaluation of learning is proper.
.746
I tend to understand and implement the concepts well.
.823
The overall quality of video and data transmission is proper.
.803
The classes are uploaded on time for our review and understanding.
.498
average online learning
.925
Extraction Method: Principal Component Analysis.
Table 2
Component Transformation Matrix
Component
1
2
3
4
5
6
7
8
9
.552
.585
.520
-.203
.090
.044
-.122
-.081
-.096
-.179
-.009
.368
.539
.608
-.129
.367
-.117
-.085
-.294
.508
-.121
-.167
-.148
-.521
.396
.389
-.106
-.145
.115
.180
.107
-.101
.724
.219
.575
.092
.640
-.021
-.357
.467
-.261
-.016
.392
.039
.129
.228
-.211
.163
.222
-.112
.111
.877
-.131
.020
.351
.554
-.479
-.259
-.475
.174
-.008
.236
-.547
.472
-.278
-.146
-.284
.321
.237
.291
-.236
.184
.170
.028
-.471
.158
.383
-.631
.298
Rotation Method: Varimax with Kaiser Normalization.
Table 3
Component Score Coefficient Matrix
.145
.080
-.064
.357
-.097
-.126
-.099
-.252
.058
-.165
.135
.106
.001
-.177
-.316
.175
-.180
-.027
.253
.113
-.118
-.090
.035
-.005
-.010
-.104
.048
-.042
-.066
.026
.066
-.061
-.044
.581
.031
.108
.212
-.031
.021
.101
.335
.040
-.110
.500
-.078
.072
-.032
-.037
-.079
.012
-.022
.342
-.058
-.123
.025
.007
.339
-.140
.030
.041
.062
-.080
-.107
.130
-.033
.042
-.413
-.034
.073
-.045
.029
.024
.249
.120
-.264
.015
-.070
.325
-.128
-.109
.065
-.024
-.072
.095
.099
-.006
.043
-.152
-.346
.153
.220
.445
-.247
.018
-.102
.068
-.017
.002
-.030
-.356
.074
.004
-.023
-.114
.155
-.091
-.063
.027
.492
-.201
-.067
-.025
.413
-.060
.009
-.065
.049
-.139
-.036
.515
-.011
.235
.267
.045
.008
.105
.061
.006
.114
.053
-.120
-.055
.013
.102
.011
.343
-.221
.338
-.047
.154
.437
.084
-.093
.071
.264
.060
.059
.183
.096
-.039
.016
.117
.081
.046
Component Scores.
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