Course provides students with a basic knowledge of missing data analysis, beginning with the types of missing data mechanisms (e.g., missing completely at random). We then discuss the problems with ignoring missing data and examine problems with conventional fixes. Single imputation with noise is contrasted with multiple imputation approaches. Real examples from policy research are given throughout. More advanced topics include pattern mixture models and handling data that are not missing at random.