Example 29.13 Confirmatory Factor Models: Some Variations
This example shows how you can fit some variations of the basic confirmatory factor analysis model by the FACTOR modeling
language. You apply these models to the scores
data set that is described in Example 29.12. The data set contains six test scores of verbal and math abilities. Thirtytwo students take the tests. Tests x1
–x3
test their verbal skills and tests y1
–y3
test their math skills.
In classical measurement theory, test items for a latent factor are parallel if they have the same loadings on the factor
and the same error variances (or reliability). Suppose for the scores
data, the items within each of the verbal
and the math
factors are parallel. You can use the following path diagram to represent such a parallel tests model:
In the path diagram, the variances of the verbal
and the math
are both fixed at 1, as indicated by the constants 1.0 adjacent to the doubleheaded arrows that are attached to factors.
You label all the singleheaded paths in the path diagram by parameter names. For the three paths (loadings) from the verbal
factor, you use the same parameter name load1
. This means that these loadings are the same parameter. You also label the doubleheaded arrows that are attached to x1
–x3
by the parameter name evar1
. This means that the corresponding error variances for these three observed variables are exactly the same. Hence, x1
–x3
are parallel tests for the verbal
factor, as required by the current confirmatory factor model.
Similarly, you define parallel tests y1
–y3
for the math
factor by using load2
as the common factor loading parameter and evar2
as the common error variances for the observed variables.
Corresponding to this path diagram, you can specify the model by the following FACTOR model specification of PROC CALIS:
proc calis data=scores;
factor
verbal ===> x1x3 = load1 load1 load1,
math ===> y1y3 = load2 load2 load2;
pvar
verbal = 1.,
math = 1.,
x1x3 = 3*evar1,
y1y3 = 3*evar2;
run;
In each entry of the FACTOR statement, you specify the factorvariables relationships, followed by a list of parameters. For
example, the three loading parameters of x1
–x3
on the verbal
factor are all named load1
. This effectively constrains the corresponding loading estimates to be the same. Similarly, in the next entry you set equality
constraints on the loading estimates y1
–y3
on the math
factor by using the same parameter name load2
.
To make the tests parallel, you also need to constrain the error variances for each variable cluster. In the PVAR statement,
in addition to setting the factor variances to 1 for identification, you set all the error variances of x1
–x3
to be the same by using the same parameter name evar1
. The notation 3*evar1
means that you want to specify evar1
three times, one time each for the error variances for the three observed variables in the variable list of the entry. Similarly,
you set the equality of the error variances of y1
–y3
by using the same parameter name evar2
.
Output 29.13.2 shows some fit indices of the parallel tests model for the scores
data. The model fit chisquare is 26.128 (df = 16, p = 0.0522). The SRMR value is 0.1537 and the RMSEA value is 0.1429. All these indices show that the model does not fit very
well. However, Bentler’s CFI is 0.9366, which shows a good model fit.
Output 29.13.2: Model Fit of the Parallel Tests Model: Scores Data
26.1283 
16 
0.0522 
0.1537 
0.1429 
0.9366 
Output 29.13.3 shows the parameter estimates of the parallel tests model. The first table of Output 29.13.3 shows the required factor pattern for parallel tests. Variables x1
–x3
all have the same loading estimates on the verbal
factor, and variables y1
–y3
all have the same loading estimates on the math
factor. All loading estimates are statistically significant.
Output 29.13.3: Parameter Estimates of the Parallel Tests Model: Scores Data
5.4226 
0.7655 
7.0833 
[load1] 


5.4226 
0.7655 
7.0833 
[load1] 


5.4226 
0.7655 
7.0833 
[load1] 



4.4001 
0.5926 
7.4246 
[load2] 


4.4001 
0.5926 
7.4246 
[load2] 


4.4001 
0.5926 
7.4246 
[load2] 


0.5024 
0.1497 
3.3569 
[_Add1] 

0.5024 
0.1497 
3.3569 
[_Add1] 


evar1 
9.61122 
1.72623 
5.56776 
evar1 
9.61122 
1.72623 
5.56776 
evar1 
9.61122 
1.72623 
5.56776 
evar2 
3.46673 
0.62264 
5.56776 
evar2 
3.46673 
0.62264 
5.56776 
evar2 
3.46673 
0.62264 
5.56776 
In the second table of Output 29.13.3, the factor covariance (or correlation) estimate is 0.5024, showing moderate relationship between the verbal
and the math
factors. The last table of Output 29.13.3 shows the error variances of the variables. As required by the parallel tests model, the error variance estimates of x1
–x3
are all 9.6112, and the error variance estimates of y1
–y3
are all 3.4667.
The TauEquivalent Tests Model
Because the parallel tests model does not fit well, you are looking for a less constrained model for the scores
data. The tauequivalent tests model is such a model. It requires only the equality of factor loadings but not the equality
of error variances within each factor. The following path diagram represents the tauequivalent tests model for the scores
data:
This path diagram is much the same as that for the parallel tests model except that now you do not use parameter names to
label the doubleheaded arrows that are attached to the observed variables. This means that you allow the corresponding error
variances to be free parameters in the tauequivalent tests model. You can use the following FACTOR model specification of
PROC CALIS to specify the tauequivalent tests model for the scores
data:
proc calis data=scores;
factor
verbal ===> x1x3 = load1 load1 load1,
math ===> y1y3 = load2 load2 load2;
pvar
verbal = 1.,
math = 1.;
run;
This specification is the same as that for the parallel tests model except that you remove the specifications about the error
variances in the PVAR statement in the current tauequivalent model. This effectively allows the error variances of the observed
variables to be (default) free parameters in the model.
Output 29.13.5 shows some model fit indices of the tauequivalent tests model for the scores
data. The chisquare is 22.0468 (df = 12, p = 0.037). The SRMR is 0.1398 and the RMSEA is 0.1643. The comparative fit index (CFI) is 0.9371. Except for the CFI value,
all other values do not support a good model fit. This model has a degrees of freedom of 12, which is less restrictive (has
more parameters) than the parallel tests model, which has a degrees of freedom of 16, as shown in Output 29.13.2. However, it seems that the tauequivalent tests model is still too restrictive for the data.
Output 29.13.5: Model Fit of the TauEquivalent Tests Model: Scores Data
22.0468 
12 
0.0370 
0.1398 
0.1643 
0.9371 
Output 29.13.6 shows the parameter estimates. The first table of Output 29.13.6 shows the required pattern of factor loadings under the tauequivalent tests model. The third table of Output 29.13.6 shows the error variance estimates. The error variance parameters are no longer constrained under the tauequivalent tests
model. Each has a unique estimate.
Output 29.13.6: Parameter Estimates of the TauEquivalent Tests Model: Scores Data
5.2418 
0.7374 
7.1085 
[load1] 


5.2418 
0.7374 
7.1085 
[load1] 


5.2418 
0.7374 
7.1085 
[load1] 



4.4462 
0.5932 
7.4953 
[load2] 


4.4462 
0.5932 
7.4953 
[load2] 


4.4462 
0.5932 
7.4953 
[load2] 


0.4514 
0.1569 
2.8772 
[_Add1] 

0.4514 
0.1569 
2.8772 
[_Add1] 


_Add2 
13.05681 
4.19549 
3.11210 
_Add3 
10.80421 
3.70322 
2.91752 
_Add4 
5.43527 
2.72147 
1.99719 
_Add5 
3.29858 
1.24673 
2.64578 
_Add6 
1.90435 
1.02393 
1.85984 
_Add7 
5.09724 
1.61477 
3.15663 
The Partially Constrained Parallel Tests Model
Because both the parallel tests and tauequivalent tests models do not fit the data well, you can explore an alternative
model for the scores
data. Suppose that for each factor only two (but not all) of their measured variables (tests) are parallel. For example,
suppose you know that tests x1
and x2
are very similar to each other (for example, both are speeded tests with forcedchoice answers), while x3
is a little different in the way it is administered (for example, openended questions). Although all tests are designed
for measuring the verbal
factor, only x1
and x2
are parallel tests while x3
is congeneric to the verbal
factor. Similarly, suppose you can argue that y2
and y3
are parallel tests while y1
is only congeneric to the math
factor.
The current modeling idea is represented by the following path diagram:
In the path diagram, x1
and x2
have the same parameter load1
for the paths from the verbal
factor. Their error variances are also the same, as labeled with the evar1
parameter adjacent to the doubleheaded arrows that are attached to the variables. The test x3
has distinct parameter names for its associated path and the attached doubleheaded arrow. The corresponding loading and
error variance parameters are alpha
and phi
, respectively. Similarly, with the use of specific parameter names, you define y2
and y3
as parallel tests for the math
factor, while y1
is congeneric to the same factor but with distinct loading and error variance parameters. Lastly, you fix the variances of
the factors to 1.0 for identification of the factor scales.
You can specify such a partially constrained parallel tests model by the following FACTOR model specification of PROC CALIS:
proc calis data=scores;
factor
verbal ===> x1x3 = load1 load1 alpha,
math ===> y1y3 = beta load2 load2;
pvar
verbal = 1.,
math = 1.,
x1x3 = evar1 evar1 phi,
y1y3 = theta evar2 evar2;
run;
First, in the FACTOR statement, you name the loading parameters that reflect the parallel tests constraints. For example,
the loading parameters of x1
and x2
on the verbal
factor are both named load1
. This means that they are the same. However, the loading parameter of x3
on the verbal
factor is named alpha
, which means that it is a separate parameter. Similarly, you apply the load2
parameter name to the loading parameters of y2
and y3
on the math
factor, but the loading parameter of y1
on the math
factor is a distinct parameter named beta
.
In the PVAR statement, the two factor variances are set to a constant 1 for the identification of latent factor scales. Next,
you use the same naming techniques as in the FACTOR statement to constrain some parts of the error variances. As a result,
together with the specifications in the FACTOR statement, x1
and x2
are parallel tests for the verbal
factor and y2
and y3
are parallel tests for the math
factor, while x3
and y1
are only congeneric tests for their respective factors.
Output 29.13.8 shows some fit indices of the partially constrained parallel tests model. The model fit chisquare is 12.6784 (df = 12, p = 0.3928). The SRMR is 0.0585 and the RMSEA is close to 0.0427. The comparative fit index (CFI) is 0.9958. All these fit
indices point to a quite reasonable model fit for the scores
data.
Output 29.13.8: Model Fit of the Partially Constrained Parallel Tests Model: Scores Data
12.6784 
12 
0.3928 
0.0585 
0.0427 
0.9958 
Notice that the current model actually has the same degrees of freedom as that of the tauequivalent tests model, as shown
in Output 29.13.5. Both models have nine parameters. But the current partially constrained parallel tests model is definitely a better model
for the data. This shows that sometimes you do not have to add more parameters to improve the model fit. Structurally different
models might explain the data quite differently, even though they might use the same number of parameters.
Output 29.13.9 show the parameter estimates of the partially constrained parallel tests model for the scores
data. The estimates in the factor loading matrix and error variances table confirm the prescribed nature of the tests—that
is, x1
and x2
are parallel tests for the verbal
factor and y2
and y3
are parallel tests for the math
factor.
Output 29.13.9: Parameter Estimates of the Partially Constrained Parallel Tests Model: Scores Data
5.8306 
0.8593 
6.7853 
[load1] 


5.8306 
0.8593 
6.7853 
[load1] 


4.6623 
0.7814 
5.9664 
[alpha] 



5.2784 
0.7010 
7.5294 
[beta] 


3.9789 
0.5732 
6.9419 
[load2] 


3.9789 
0.5732 
6.9419 
[load2] 


0.5203 
0.1425 
3.6497 
[_Add1] 

0.5203 
0.1425 
3.6497 
[_Add1] 


evar1 
10.31998 
2.57827 
4.00268 
evar1 
10.31998 
2.57827 
4.00268 
phi 
6.67832 
2.59902 
2.56956 
theta 
0.80714 
1.35247 
0.59679 
evar2 
4.07534 
1.00371 
4.06028 
evar2 
4.07534 
1.00371 
4.06028 