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Multiple-Group Analysis
ENow 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
EClose the output viewer.
EFrom the menus, choose Diagram > Draw Covariances.
EClick and drag to draw a double-headed arrow between eps2 and eps4.
EFrom the menus, choose Analyze > Multiple-Group Analysis, and click OK in the message
box that appears.
EIn the Multiple-Group Analysis dialog box, click OK.
EFrom the menus, choose Analyze > Calculate Estimates to fit all models.
EFrom the menus, choose View > Text Output.
EUse 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
χ23.98=
df 5=