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Multiple-Group Factor Analysis
Assuming model Measurement intercepts to be correct, the following table shows that
this chi-square difference is significant:
In the preceding two tables, two chi-square statistics and their associated degrees of
freedom are especially important. The first, with , allowed
accepting the hypothesis of equal intercepts and equal regression weights in the
measurement model. It was important to establish the credibility of this hypothesis
because, without equal intercepts and equal regression weights, it would be unclear
that the factors have the same meaning for boys as for girls and so there would be no
interest in comparing their means. The other important chi-square statistic,
with , leads to rejection of the hypothesis that boys and girls have the same
factor means.
Group differences between the boys’ and girls’ factor means can be determined
from the girls’ estimates in the Measurement intercepts model.
E Select the Measurement intercepts model in the pane at the lower left of the output
viewer.
E In the navigation tree, click Estimates, then Scalars, and then Means.
The boys’ means were fixed at 0, so only the girls’ means were estimated, as shown in
the following table:
These estimates were discussed in Model A of Example 15, which is identical to the
present Measurement intercepts model. (Model B of Example 15 is identical to the
present Structural means model.)
Model DF CMIN P
NFI
Delta-1
IFI
Delta-2
RFI
rho-1
TLI
rho2
Structural means 2 8.030 0.018 0.024 0.026 0.021 0.023
Structural covariances 5 11.787 0.038 0.035 0.038 0.022 0.024
Measurement residuals 11 15.865 0.146 0.047 0.051 0.014 0.015
Estimate S.E. C.R. P Label
spatial –1.066 0.881 –1.209 0.226 m1_1
verbal 0.956 0.521 1.836 0.066 m2_1
χ
2
22.59=
df 24=
χ
2
8.03=
df 2=