Lecture 2.1.5.L: Multilevel Models & Hierarchical Data | Masters in Health Data Science

In Lecture 2.1.5 of the Masters in Health Data Science, we address a critical limitation of traditional statistical models: the false assumption that all data points are independent.

This lecture introduces Multilevel Models (Hierarchical Models) and explains why clustered and nested data—such as patients within hospitals or students within schools—require specialized modeling approaches. You will learn how ignoring hierarchical structure leads to biased estimates, inflated Type I errors, and misleading conclusions.

Key concepts covered include fixed effects vs. random effects, intra-class correlation (ICC), group-level variability, and cross-level interactions, all explained with intuitive examples from healthcare, education, and real-world analytics.

This lecture lays the foundation for correctly modeling complex, real-world health data where context matters.

Ideal for:
Master’s students, health data analysts, biostatisticians, and researchers working with clustered or grouped healthcare data.

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Видео Lecture 2.1.5.L: Multilevel Models & Hierarchical Data | Masters in Health Data Science канала Universal Digital Health