Multilevel Models: Understanding Nested Data Structures

This lecture covers the fundamental concepts of multilevel models, focusing on nested data structures with multiple levels such as schools, classes, and students. It explains the importance of considering intra-class correlation to measure outcome correlation within clusters. The instructor discusses the implications of ignoring the multi-level data structure and demonstrates how to calculate the intra-class correlation. Additionally, the lecture explores random-intercept and random-slope models, highlighting the significance of including predictors in the models. Practical examples and data exploration techniques are provided to illustrate the application of multilevel models in real-world scenarios.