Missing data are ubiquitous in nearly every data analytic enterprise. Simple ad-hoc techniques for dealing with missing values such as deleting incomplete cases or replacing missing values with the item mean can cause a host of (hidden) problems. In this workshop, we will discuss principled methods for treating missing data and how to apply these methods in R. We will cover some basic missing data theory and two principled methods for treating missing data: multiple imputation (MI) and full information maximum likelihood (FIML). Participants will practice what they learn via practical exercises.
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This 4-day hybrid course by the MICE developers teaches you the basics in solving your own missing data problems appropriately. Participants will learn how to form imputation models, how to combine data sets, how to model non-response, how to use diagnostics to inspect the imputed values, how to obtain valid inference on incomplete data and how to avoid many of the pitfalls associated with real-life missing data problems. While there will be plenty of opportunity to ask the experts for help and advice throughout the course, we end the course with the opportunity to consult us on your own specific missing data problem.