Incomplete 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 the former is focused on subsequently. At the software level, the popular package Mplus is utilized on several 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 analysis of incomplete data, and is characterized by an applied missing data analysis direction focusing predominantly on the maximum likelihood approach.