Multilevel and Mixed Models Using Stata – May 2023

Event Phone: 1-610-715-0115

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A 3-Day Livestream Seminar
Taught by Stephen Vaisey, Ph.D.

This seminar provides an intensive introduction to multilevel models, 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.

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 attributed to different levels of observation. We will then move on to mixed models (random effects models with fixed covariates) that allow us to ask how factors at different levels can affect an outcome.

Next, we will investigate how using random coefficients and cross-level interactions can help us discover hidden structure in our data and help us investigate how individual-level processes work differently in different contexts. We will also briefly consider how these techniques can be applied to cases where we have repeated observations of individuals or other entities over time.

Although the course will focus primarily on the continuous outcome case, we will also cover how these models can easily be extended for use with categorical and limited dependent variables.

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

    1. Know the technical and substantive difference between fixed and random effects.
    2. Understand random intercepts and random coefficients and when to use each one.
    3. Know how to combine the strengths of random-effects and fixed-effects approaches into a single “between-within” model.
    4. Know how to estimate these models and interpret the result.

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