Handling Missing Data in Longitudinal Studies Using Multiple Imputation (ICE) in Stata Explained

Missing data is a frequent and critical issue in longitudinal studies, and the approach you choose to handle it can significantly impact your results. In this video, I walk you through how to address missing values effectively using Multiple Imputation by Chained Equations (ICE) in Stata. You'll learn how to prepare your dataset, implement the ICE procedure, and interpret the results post-imputation. Whether you're a student, researcher, or data analyst, this tutorial provides a clear, practical guide to managing incomplete data and improving the robustness of your longitudinal analysis in Stata.