Great question! I get questions about missing data and how to deal with it all of the time. To your question, simply removing partial data and doubling your complete data points will not result in the same outcome as a multiple imputation. This is because a multiple imputation inserts random values (within the parameters of your data) into your dataset to make sure that no bias occurs by the replaced values being present. By contrast, if a bias exists in the pattern of missing data of your original dataset, doubling the existing values will merely magnify this bias. Multiple imputation is widely considered to be the most bias-free method of dealing with missing data. Although it can be complex and time intensive, modern statistical software (such as SPSS and AMOS) offer "multiple imputation" features, which vastly simplify the process.