What Missing Data Really Means in Applied Research (Part 2: MAR Explained for Stata Users) скачать в хорошем качестве

What Missing Data Really Means in Applied Research (Part 2: MAR Explained for Stata Users)

In applied research, most missing data are not completely random. In this video, we focus on Missing At Random (MAR)—the assumption that underpins Multiple Imputation (MI) in Stata and many journal-based analyses. Using applied examples and a Stata-based perspective, this video explains what MAR actually means, why it cannot be statistically tested, and how researchers can responsibly justify it using observed data. What you’ll learn in this video • What Missing At Random (MAR) really means in applied research • How MAR differs from MCAR and why MCAR is rarely realistic • Why MAR is an assumption—not a testable condition • How to assess MAR plausibility using observed variables in Stata • Common mistakes when applying Multiple Imputation under MAR • When MAR becomes questionable and what that implies for analysis This video is intended for graduate students, PhD researchers, policy analysts, and applied researchers working with real, messy datasets in Stata. 📩 Consultation, Training & Research Support If you need support with: • Missing data & multiple imputation in Stata • Thesis, dissertation, or journal-ready analysis • Data diagnostics and applied research methods • Private training or research consultation 📧 Email: wilfred.researchanalytics@gmail.com/ info@trakanalytica.com 🌐 Book a service: https://www.trakanalytica.com/contact... 🔔 Stay connected Subscribe for applied, defensible, and publication-oriented data analytics using Stata. #missingdata #MultipleImputation #stata #researchmethods #AppliedResearch