Understanding Dataset Types & Best Missing Data Method (ICE vs. Interpolation)

📊 Confused about how to handle missing data in different types of datasets? You're not alone! Many of my subscribers have been asking whether to use Multiple Imputation (ICE) or Linear Interpolation, and I realized there's a lot of confusion around these approaches. In this video, I break down the differences between longitudinal data, panel data, and time series data, explaining how each dataset is structured and when to apply Multiple Imputation (ICE) vs. Linear Interpolation for handling missing values. By the end of this video, you'll have a clear understanding of which method to use in your analysis! 🚀 📌 What You'll Learn: ✅ The key differences between time series, panel, and longitudinal data ✅ How to decide between Multiple Imputation (ICE) vs. Linear Interpolation ✅ Practical examples of when to apply each method in Stata 🎯 Need personalized training? Contact me for 1-on-1 coaching! 📩 Email: [email protected]