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Multiple-Group Analysis
E
Now click experimental in the panel on the left. As you can see in the following
covariance table for the experimental group, there are four modification indices greater
than 4:
Of these, only two modifications have an obvious theoretical justification: allowing
eps2 to correlate with eps4, and allowing eps1 to correlate with eps3. Between these
two, allowing eps2 to correlate with eps4 has the larger modification index. Thus the
modification indices from the control group and the experimental group both suggest
allowing eps2 to correlate with eps4.
Modifying the Model and Repeating the Analysis
E Close the output viewer.
E From the menus, choose Diagram > Draw Covariances.
E Click and drag to draw a double-headed arrow between eps2 and eps4.
E From the menus, choose Analyze > Multiple-Group Analysis, and click OK in the message
box that appears.
E In the Multiple-Group Analysis dialog box, click OK.
E From the menus, choose Analyze > Calculate Estimates to fit all models.
E From the menus, choose View > Text Output.
E Use the navigation tree to view the fit measures for the Structural weights model.
With the additional double-headed arrow connecting eps2 and eps4, the Structural
weights model has an adequate fit ( with ), as shown in the
following CMIN table:
M.I. Par Change
eps2 <--> eps4 9.314 4.417
eps2 <--> eps3 9.393 –4.117
eps1 <--> eps4 8.513 –3.947
eps1 <--> eps3 6.192 3.110
χ
2
3.98=
df 5=