Simulation of Imputation Effects Under Different Assumptions (303268)

Keywords: Missing data, imputation, survey

Missing data is something we cannot prevent when data become missing in the process of data collection. There are many reasons why data can be missing, including refusing to answer a sensitive question for fear of embarrassment. Researchers often assume their data are “missing completely at random” or “missing at random.” Unfortunately, we cannot test whether the mechanism condition is satisfied because missing values cannot be calculated. Alternatively, we can run simulation in SAS to observe the behaviors of missing data under different assumptions: missing completely at random, missing at random, and ignorability. In this poster, we will compare the effects from imputation methods if we assign a set of variable of interests to missing. The idea of imputation is to see how efficient substituted values in a data set affect further studies. This will let the audience decide which method(s) would be best to approach a data set when it comes to missing data.