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Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 2-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 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. For more on panel data, see Longitudinal Data Analysis Using R, which will be taught immediately after this course.)

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.

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 random intercepts, random coefficients, and crossed random effects models and know when to use each one.
3. Know how to combine the strengths of random-effects and fixed-effects approaches into a single model.
4. Know how to estimate these models and interpret the results.