Missing Data in R

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.

Professionals who seek a master-level introduction to missing data analysis.

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After completing this course, participants can:

1. Describe the most important characteristics of a missing data problem and choose appropriate statistics, metrics, or visualizations to quantify/illustrate those characteristics.
2. Describe multiple imputation (MI): what it is, why it works, and why it is superior to traditional, ad-hoc techniques.
3. Describe full information maximum likelihood (FIML): what it is, why it works, and why it is superior to traditional ad-hoc techniques.
4. Compare and contrast the relative strengths and weaknesses of MI and FIML.
5. Write basic R scripts to do the following:

• Explore a missing data problem with appropriate statistics, metrics, and visualizations.
• Conduct an MI-based analysis.
• Conduct a FIML-based analysis.