Multilevel and Mixed Models 

A 2-day seminar taught by Stephen Vaisey, Ph.D. 

Read reviews from this course

To see a sample of the course materials, click here.

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 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 understand how individual-level processes work differently in different contexts. We will also 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 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


To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computerThis seminar will use Stata 14 for the many empirical examples and exercises. However, no previous experience with Stata is assumed. Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy. 


This course is for anyone who want to learn to apply multilevel models to observational data. Participants should have a basic foundation in linear regression.


The seminar meets Friday, May 5 and Saturday, May 6 at the Hilton Garden Inn, Marina Del Rey, 4200 Admiralty Way, Marina Del Rey, CA 90292. The class will meet from 9 to 5 each day with a 1-hour lunch break.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking. 


The fee of $995 includes all course materials.

Refund 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). 

Lodging Reservation Instructions

A room block has been arranged at the Hilton Garden Inn, Marina Del Rey, 4200 Admiralty Way, Marina Del Rey, CA 90292. The special rate of $269 per night is available until Wednesday, April 5, 2017. You can make your reservation either online or by phone:

  • Reserve a room online by clicking here and choosing your dates. Then click on “add special rate code” underneath the dates, and type SHLLC in the middle box that says “group code”.  
  • Reserve a room by phone by calling the Hilton Garden Inn at 1-310-301-2000 and mentioning the “Statistical Horizons” room block.


  1. Review of linear regression
  2. Fixed and random effects
  3. Random intercepts model and intraclass correlation
  4. Mixed models (random intercepts models with covariates)
  5. Random coefficient models
  6. Model selection considerations
  7. Crossed random effects models
  8. Extensions to categorical and limited dependent variables

COMMENTS From Recent Participants 

Excellent coverage of material for newcomers to the topic. Lucid and accessible, providing a solid foundation for using multilevel modeling. Provided insight into the strengths and limitations of modeling assumptions, thereby reducing guesswork in application.
  David Barker, Gannon University

I liked how Dr. Vaisey broke down complex material into smaller pieces so that it would be easier for me, without a statistical background, to understand the concept.

Excellent introduction to mixed models. In his unique, vivid style, Steve provides a practical approach to learning using his vast knowledge in an open, ready-for-business way, helping researchers think more clearly about the complexity of their data.
 Ariel Knafo-Noam, Hebrew University of Jerusalem