Based off the provided image, do the measuring scales exhibit sufficient convergent and discriminant validity? Explain why or why not.

Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter4: Linear Programming Models
Section: Chapter Questions
Problem 91P
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Based off the provided image, do the measuring scales exhibit sufficient convergent and discriminant validity? Explain why or why not.

Rotated Factor Matrix
Factor
1
2
3
4
5
OBM1
.912
.185
.023
-.003
-.044
OBM2
.935
.183
.026
-.013
-.095
OBM3
.855
.231
-.035
-015
-.059
OBM4
.846
.228
-.084
-.010
-.029
CA1
-.113
.024
.087
.054
.683
CA2
.068
-.110
.137
.122
.585
CA3
.019
-.122
.570
.067
.339
CA4
.072
-.164
.622
.026
.410
CA6
-.128
.043
.144
.108
.365
CA6
.022
-.186
.392
.781
.176
CA7
-.018
-.091
.322
.774
.198
-.03
CAB
-.125
.371
.369
.315
-.080
-.156
.802
.268
.085
CA10
-.040
-.173
.823
.306
.053
CA11
-.063
-.266
.670
.391
.114
W01
.250
.715
-.065
-.123
-.041
WO2
.124
.757
-.066
-.138
.075
W03
.151
.717
-.210
-.067
-.110
W04
.109
.750
-.170
-.045
-.018
W05
.265
.701
-208
-.005
-.078
Extraction Method: Maximum Likelihood.
Rotation Method: Varimax with Kaiser Nomalization.
Transcribed Image Text:Rotated Factor Matrix Factor 1 2 3 4 5 OBM1 .912 .185 .023 -.003 -.044 OBM2 .935 .183 .026 -.013 -.095 OBM3 .855 .231 -.035 -015 -.059 OBM4 .846 .228 -.084 -.010 -.029 CA1 -.113 .024 .087 .054 .683 CA2 .068 -.110 .137 .122 .585 CA3 .019 -.122 .570 .067 .339 CA4 .072 -.164 .622 .026 .410 CA6 -.128 .043 .144 .108 .365 CA6 .022 -.186 .392 .781 .176 CA7 -.018 -.091 .322 .774 .198 -.03 CAB -.125 .371 .369 .315 -.080 -.156 .802 .268 .085 CA10 -.040 -.173 .823 .306 .053 CA11 -.063 -.266 .670 .391 .114 W01 .250 .715 -.065 -.123 -.041 WO2 .124 .757 -.066 -.138 .075 W03 .151 .717 -.210 -.067 -.110 W04 .109 .750 -.170 -.045 -.018 W05 .265 .701 -208 -.005 -.078 Extraction Method: Maximum Likelihood. Rotation Method: Varimax with Kaiser Nomalization.
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