Multilevel Modeling & Causal Graphs

Lectures

Mixed Effects Models Part 1: Introduction to Multilevel Modeling

Discusses mixed-effects models (also referred to as multilevel models) and introduces the concepts of nested and crossed effects. Also discusses key considerations of model selection, including decisions between fixed and random intercepts and fixed and random slopes.

Mixed Effects Models Part 2: Advanced Considerations in Multilevel Modeling

Discusses advanced considerations in the context of multilevel modeling, including convergence issues, model comparison, and assumption tests. A brief discussion of confidence interval calculations is also included. Note that Part 2 assumes pre-existing knowledge of the basic concepts and R code behind multilevel modeling. For more basic information, see Part 1.

Causal Graphs

Introduces the concept of causal graphs—precise visual representations of hypothesized causal relationships between variables. Also introduces the Chield database for understanding and comparing causal hypotheses between researchers.