Missing data are a common challenge in real-world datasets, and handling them incorrectly can bias results or reduce statistical power.

Dates: 22–24 April | Online

This 3-day online course will teach participants how to:

• Identify missingness mechanisms (MCAR, MAR, MNAR)

• Diagnose and visualize missing data patterns in R

• Apply traditional methods (listwise/pairwise deletion, simple imputation)

• Implement modern imputation strategies, including MICE, Soft Impute, and Expectation–Maximization (EM)

• Choose the right method based on data structure and analytical goals

The course combines theory with practical exercises to ensure participants can apply the methods to real datasets.

More info & registration: https://www.physalia-courses.org/courses-workshops/missing-data-with-r/