A two-day seminar taught by Stephen Vaisey, Ph.D.

Multilevel models are a class of regression models for data that have a hierarchical (or nested) structure. Common examples of such data structures are students nested within schools or classrooms, patients nested within hospitals, or survey respondents nested within countries. Using regression techniques that ignore this hierarchical structure (such as ordinary least squares) can lead to incorrect results because such methods assume that all observations are independent. Perhaps more important, using inappropriate techniques (like pooling or aggregating) prevents researchers from asking substantively interesting questions about how processes work at different levels.

This two-day seminar provides an intensive introduction to multilevel models. After a brief conceptual introduction (including a discussion of the difference between random and fixed effects), we will begin with simple variance components models that can tell us how much of the variation in a measure can be assigned to different levels. We will then move on to mixed models (random effects models with fixed covariates) that allow us to ask how both individual-level and higher-level factors affect an outcome. Next, we will investigate how using random coefficients can help us model how individual-level processes work differently in different social contexts. Finally, we will use the example of hierarchical age-period-cohort models to explore how we can use crossed random effects to model more complicated forms of dependence.

Although the course will focus primarily on the continuous outcome case, we will also briefly cover how these models can easily be extended for use with categorical and limited dependent variables. We will also touch on some of the connections between multilevel models and models for panel data.

The seminar will focus on hands-on understanding and draw from examples across the social and behavioral sciences. At the conclusion of the course, students will:

1. Know the technical and substantive difference between fixed and random effects
2. Understand what random intercept models, random coefficient models, and crossed random effects models are and when to use each one
3. Know how to estimate and interpret these models in Stata