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 ...

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