Application: Nonrandom Missingness and Imputation
Let’s examine the effect
of nonrandom missingness on our results and the potential improvement
offered by the EM algorithm. Using the same SDQ sample of 300 students
that we used earlier, we simulated some nonrandom, arbitrary missingness
by recoding values of “6” to system missing values for
the first English item (Eng1: I learn things quickly in English classes).
This created a biased sample eliminating those students who answered
the most optimistically about their learning in English (76 out of
300 cases).
We then used
the
MI procedure to estimate the EM covariance
matrix. This covariance matrix is requested on the EM statement. We use all of the variables to produce the final estimates by ...Get Exploratory Factor Analysis with SAS now with the O’Reilly learning platform.
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