Amos Missing Values Application

Estimation with missing values application

In this example, a missing at random mechanism causes values of the dependent variable to be missing. Use of ordinary regression leads to biased estimation, while use of Amos leads to good estimates.

For a sample size of 400, consider two standardized variables, X and Y, that are correlated at 0.6. In fact, the equation relating X to Y is:

Y = 4 + 0.6*X + error

Next, suppose that some observations on Y are missing. The mechanism for whether Y is missing or observed is a Missing At Random mechanism:

compute ind=0.
compute u=uni(10).
if ( (x > 0) and (u < 4) ) ind=1.
compute ymiss=y.
if (ind=1) ymiss=-999.

That is, whether or not Y is missing is contingent on the value of X. If X is negative, Y is complete, and if X is positive, there is about a 40% chance that Y is randomly missing. The resulting dependent variable is called YMISS.

The descriptive statistics for X and YMISS are shown here.

Note that X has 400 observations, a mean of 0, and a standard deviation of 1, while YMISS has 331 nonmissing observations.

Suppose you regress YMISS on X. Here are the regression results.

The descriptive statistics for X and YMISS are shown here.

You can specify regression in Amos as follows.

X and YMISS are both observed, while the regression error term is unobserved. The parameter value from the error term to YMISS is set to 1, while the parameter for the path from X to YMISS is to be estimated. In Amos, select View/Set Analysis Properties, select the estimation tab, and check "Estimate means and intercepts."