The CALIS Procedure

Suppose you are interested in testing the hypothesis that the effect of Fx on Fy (that is, the regression slope) is 1. The following path diagram represents the model under the hypothesis:

Output 27.9.4:

Now you label the path from Fx to Fy with a fixed constant 1, which reflects the hypothesis you want to test. You can transcribe the current path diagram easily into the following PROC CALIS specification:

proc calis data=measures;
   path
      x  <=== Fx  = 1.,
      Fx ===> Fy  = 1.,  /* Testing a fixed constant effect */
      Fy ===> y   = 1.;
   pvar
      x  = evar,
      y  = evar,
      Fy = 0.,
      Fx;
run;

Output 27.9.5 shows the model fit chi-square statistic. The model fit chi-square test here essentially is a test of the null hypothesis of the constant effect at 1 because the alternative hypothesis is a saturated model. The chi-square value is 8.1844 (df = 1, p = .0042), which is statistically significant. This means that the hypothesis of constant effect at 1 is rejected.

Output 27.9.6 shows the estimates under this restricted model. In the first table of Output 27.9.6, all path effects or coefficients are fixed at 1. In the second tale of Output 27.9.6, estimates of the error variances are 0.1255 (standard error = 0.0330) for both x and y. The error variance of Fy is a fixed zero, as required in the hypothesis. The variance estimate of Fx is 0.9205 (standard error = 0.2587).

Suppose you are interested in testing the hypothesis that the intercept for the regression of Fy on Fx is zero, while the regression effect is freely estimated. Because the intercept parameter belongs to the mean structures, you need to specify this parameter in PROC CALIS to test the hypothesis.

There are two ways to include the mean structure analysis. First, you can include the MEANSTR option in the PROC CALIS statement. Alternatively, you can use the MEAN statement to specify the means and intercepts in the model. The following statements specify the model under the zero intercept hypothesis:

proc calis data=measures;
   path
      x  <=== Fx  = 1.,
      Fx ===> Fy  ,       /* regression effect is freely estimated */
      Fy ===> y   = 1.;
   pvar
      x  = evar,
      y  = evar,
      Fy = 0.,
      Fx;
   mean
      x y = 0. 0., /* Intercepts are zero in the measurement error model */
      Fy  = 0.,    /* Fixed to zero under the hypothesis */
      Fx;          /* Mean of Fx is freely estimated */
run;

In the PATH statement, the regression effect of Fx on Fy is freely estimated. In the MEAN statement, you specify the means or intercepts of the variables. Each variable in your measurement error model has either a mean or an intercept (but not both) to specify. If a variable is exogenous (independent), you can specify its mean in the MEAN statement. Otherwise, you can specify its intercept in the MEAN statement. Variables x and y in the measurement error model are both endogenous. They are measured indicators of their corresponding true scores Fx and Fy. Under the measurement error model, their intercepts are fixed zeros. The intercept for Fy is zero under the current hypothesized model. The mean of Fx is freely estimated under the model. This parameter is specified in the MEAN statement but is not named.

Output 27.9.7 shows the model fit chi-square statistic. The chi-square value is 10.5397 (df = 1, p = .0012), which is statistically significant. This means that the zero intercept hypothesis for the regression of Fy on Fx is rejected.

Output 27.9.8 shows the estimates under the hypothesized model. The effect of Fx on Fy is 1.8169 (standard error = 0.0206). In the last table of Output 27.9.8, the estimate of the mean of Fx is 6.9048 (standard error = 0.1388). The intercepts for all other variables are fixed at zero under the hypothesized model.

In the preceding model, you fit a restricted regression model with a zero intercept. You reject the null hypothesis and conclude that this intercept is significantly different from zero. The alternative hypothesis is a saturated model with the intercept freely estimated. The model under the alternative hypothesis is specified in the following statements:

proc calis data=measures;
   path
      x  <=== Fx  = 1.,
      Fx ===> Fy  ,
      Fy ===> y   = 1.;
   pvar
      x  = evar,
      y  = evar,
      Fy = 0.,
      Fx;
   mean
      x y = 0. 0.,
      Fy Fx;
run;

Output 27.9.9 shows that model fit chi-square statistic is zero. This is expected because you are fitting a measurement error model with saturated mean and covariance structures.

Output 27.9.10 shows the estimates under the measurement model with saturated mean and covariance structures. The effect of Fx on Fy is 1.3028 (standard error=0.1134), which is considerably smaller than the corresponding estimate in the restricted model with zero intercept, as shown in Output 27.9.8. The intercept estimate of Fy is 3.5800 (standard error = 0.7864), with a significant t value of 4.55.

In this example, you fit some measurement error models with some parameter constraints that reflect the hypothesized models of interest. You can set equality constraints by simply providing the same parameter names in the PATH model specification of PROC CALIS. You can also fix parameters to constants. In the MEAN statement, you can specify the intercepts and means of the variables in the measurement error models. You can apply all these techniques to more complicated measurement error models with multiple predictors, as shown in Example 27.10, where you also fit measurement error models with correlated errors.