This chapter reviews several methods for addressing address the statistical problem of missing data. We first explain how missing data can affect different components of the study design and the statistical analyses in such a way that the validity of the findings may become questionable. We next describe several methods to address the missing data problem and show why some may be problematic. We explain why multiple imputation (MI) and maximum likelihood (ML) are the preferred methods for addressing missing data issues. We then present an example using Stata, focusing on one of the preferred methods, multiple imputation. Lastly, within the context of an analysis of adolescent pregnancy, we use several methods to handle missing data and show how the analysis results may differ depending on which missing data method is used.