Incomplete data sets (at times also referred to as “missing data sets”) pervade the social, behavioral, biomedical, educational, business, marketing, and economics disciplines. This workshop discusses initially patterns and mechanisms of missing data, and subsequently the flaws of traditional methods for ‘dealing’ with missing data. Two modern, principled and state-of-the art methods are then referred to – maximum likelihood (full information maximum likelihood) and multiple imputation – and subsequently discussed in considerable detail as well as exemplified. At the software level, the popular latent variable modeling package Mplus is utilized on multiple occasions. Throughout the workshop, empirical examples are repeatedly used. The workshop is aimed at graduate students and researchers with limited or no prior knowledge of methods for the analysis of incomplete data (or missing data analysis methods), and is characterized by an applied missing data analysis direction focusing predominantly on the maximum likelihood approach.