Dealing with missing survey data in longitudinal analysis

This paper reviews techniques for dealing with missing data from complex surveys when conducting longitudinal analysis. In addition to incurring the same types of missingness as cross sectional data, longitudinal observations also suffer from drop out missingness. For the purpose of analyzing longitudinal data, random effects models are most often used to account for the longitudinal nature of the data. However, there are difficulties in incorporating the complex design with typical multi-level models that are used in this type of longitudinal analysis, especially in the presence of drop-out missingness.