| Longitudinal data analysis has become a popular statistical method. In order to produce accurate analysis, it is better to analyze longitudinal data with a complete dataset. Even in well-controlled situations, missing data often occurs in longitudinal studies. That is why the imputation methods are important and essential to learn. The effectiveness of these methods for different data structures has not been well studied. In this dissertation, four commonly used imputation methods are compared. Then, a simulation study and datasets in literature are conducted to evaluate the performance of imputation methods under a variety of circumstances. When the complete dataset is available, Missing Completely at Random (MCAR) is created and imputation methods are used to predict the missing values and analyze the mean of empirical means for each dataset. The experiments are concluded by outlining the conditions for each imputation method to produce reasonable and efficient statistical analysis. This dissertation emphasizes the need for improving the methodology for handling missing data when using imputations methods in longitudinal analysis. |
