Filter
Top Products
230
Example 15
The identification status of the factor analysis model is a difficult subject when
estimating factor means. In fact, Sörbom’s accomplishment was to show how to
constrain parameters so that the factor analysis model is identified and so that
differences in factor means can be estimated. We will follow Sörbom’s guidelines for
achieving model identification in the present example.
About the Data
We will use the Holzinger and Swineford (1939) data from Example 12. The girls’
dataset is in Grnt_fem.sav. The boys’ dataset is in Grnt_mal.sav.
Model A for Boys and Girls
Specifying the Model
We need to construct a model to test the following null hypothesis: Boys and girls have
the same average spatial ability and the same average verbal ability, where spatial and
verbal ability are common factors. In order for this hypothesis to have meaning, the
spatial and the verbal factors must be related to the observed variables in the same way
for girls as for boys. This means that the girls’ regression weights and intercepts must
be equal to the boys’ regression weights and intercepts.
Model B of Example 12 can be used as a starting point for specifying Model A of
the present example. Starting with Model B of Example 12:
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).
The regression weights are already constrained to be equal across groups. To begin
constraining the intercepts to be equal across groups:
E Right-click one of the observed variables, such as visperc.
E Choose Object Properties from the pop-up menu.