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Example 9
chi-square statistic that will occur if the corresponding constraint—and only that
constraint—is removed.
The following raw parameter estimates are difficult to interpret because they would
have been different if the identification constraints had been different:
As expected, the covariance between eps2 and eps4 is positive. The most interesting
result that appears along with the parameter estimates is the critical ratio for the effect
of treatment on post_verbal. This critical ratio shows that treatment has a highly
significant effect on post_verbal. We will shortly obtain a better test of the significance
of this effect by modifying Model B so that this regression weight is fixed at 0. In the
meantime, here are the standardized estimates and the squared multiple correlations as
displayed by Amos Graphics:
Regression Weights: (Group number 1 - Default model)
Estimate S.E. C.R. P Label
post_verbal
<---
pre_verbal
.889 .053 16.900 ***
post_verbal
<---
treatment 3.640 .477 7.625 ***
pre_syn <---
pre_verbal
1.000
pre_opp <---
pre_verbal
.881 .053 16.606 ***
post_syn <---
post_verbal
1.000
post_opp <---
post_verbal
.906 .053 16.948 ***
Covariances: (Group number 1 - Default model)
Estimate S.E. C.R. P Label
pre_verbal
<-->
treatment
.467 .226 2.066 .039
eps2 <-->
eps4 6.797 1.344 5.059 ***
Variances: (Group number 1 - Default model)
Estimate S.E. C.R. P Label
pre_verbal
38.491 4.501 8.552 ***
treatment
.249 .024 10.296 ***
zeta
4.824 1.331 3.625 ***
eps1
6.013 1.502 4.004 ***
eps2
12.255 1.603 7.646 ***
eps3
6.546 1.501 4.360 ***
eps4
14.685 1.812 8.102 ***