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Example 5
split exactly in half. As a result, 2satisfaction is 20% longer than 1satisfaction.
Assuming that the tests differ only in length leads to the following conclusions:
The regression weight for regressing 2satisfaction on satisfaction should be 1.2
times the weight for regressing 1satisfaction on satisfaction.
Given equal variances for error7 and error8, the regression weight for error8
should be times as large as the regression weight for error7.
You do not need to redraw the path diagram from scratch in order to impose these
parameter constraints. You can take the path diagram that you created for Model A as
a starting point and then change the values of two regression weights. Here is the path
diagram after those changes:
Results for Model B
The additional parameter constraints of Model B result in increased degrees of freedom:
1.2 1.095445=
1knowledge
2knowledge
1value
2value
1satisfaction
2satisfaction
1performance
2performance
knowledge
value
satisfaction
performance
alpha
error1
alpha
error2
delta
error8
delta
error7
gamma
error6
gamma
error5
beta
error4
beta
error3
1
1
1
1
1
1.095445
1.2
1
1
1
1
1
1
1
1
1
error9
1
Example 5: Model B
Parallel tests regression
Job performance of farm managers
Warren, White and Fuller (1974)
Model Specification