Missing observations in dependent variable is a common feature in survey
research. A number of techniques have been developed to impute missing
data. In this article, we have evaluated the performance of several imputation
methods namely mean-before method, mean-before-after method and
expectation-maximization algorithm in linear structural relationship model.
On the basis of mean absolute error and root mean square error for both
simulated and real data sets, we have shown that expectation-maximization
algorithm is the most effective method than the other two imputation methods
to analyze the missing data in linear structural relationship model.