Based off the provided image, do the measuring scales exhibit sufficient convergent and discriminant validity? Explain why or why not. Rotated Factor MatrixFactor12345OBM1.912.185.023-.003-.044OBM2.935.183.026-.013-.095OBM3.855.231-.035-015-.059OBM4.846.228-.084-.010-.029CA1-.113.024.087.054.683CA2.068-.110.137.122.585CA3.019-.122.570.067.339CA4.072-.164.622.026.410CA6-.128.043.144.108.365CA6.022-.186.392.781.176CA7-.018-.091.322.774.198-.03CAB-.125.371.369.315-.080-.156.802.268.085CA10-.040-.173.823.306.053CA11-.063-.266.670.391.114W01.250.715-.065-.123-.041WO2.124.757-.066-.138.075W03.151.717-.210-.067-.110W04.109.750-.170-.045-.018W05.265.701-208-.005-.078Extraction Method: Maximum Likelihood.Rotation Method: Varimax with Kaiser Nomalization.

Small Introduction about SC and DV Convergent validity demonstrates the relationship between two…

Answered: Based off the provided image, do the…

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.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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Question

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.

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