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Example 17
After specifying the data file to be Grant_x.sav and drawing the above path diagram:
E From the menus, choose View > Analysis Properties.
E In the Analysis Properties dialog box, click the Estimation tab.
E Select Estimate means and intercepts (a check mark appears next to it).
This will give you an estimate of the intercept in each of the six regression equations
for predicting the measured variables. Maximum likelihood estimation with missing
values works only when you estimate means and intercepts, so you have to estimate
them even if you are not interested in the estimates.
Saturated and Independence Models
Computing some fit measures requires fitting the saturated and independence models in
addition to your model. This is never a problem with complete data, but fitting these
models can require extensive computation when there are missing values. The saturated
model is especially problematic. With p observed variables, the saturated model has
parameters. For example, with 10 observed variables, there are 65
parameters; with 20 variables, there are 230 parameters; with 40 variables, there are 860
parameters; and so on. It may be impractical to fit the saturated model because of the
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wordmean
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err_c
err_l
err_p
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Example 17, Model A
Factor analysis with missing data
Holzinger and Swineford (1939): Girls' sample
Model Specification
pp3+()× 2⁄