Multilevel and Mixed Models
A 2-day seminar taught by Stephen Vaisey, Ph.D.
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.
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:
- Know the technical and substantive difference between fixed and random effects
- Understand random intercepts, random coefficients, and crossed random effects models and know when to use each one
Know How to combine the strengths of random-effects and fixed-effects approaches into a single model
- Know how to estimate these models and interpret the results
To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer. The vast majority of what you will learn in this course can be applied in any software package. This seminar will mostly use Stata 14 for empirical examples and exercises. However, no previous experience with Stata is needed.
WHO SHOULD ATTEND?
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.
Location, FORMAT, AND MATERIALS
The class will meet from 9 am to 5 pm each day with a 1-hour lunch break from 12 pm to 1 pm at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
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.
REGISTRATION AND LODGING
The fee of $995 includes all course materials. The early registration fee of $895.00 is available until November 1.
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 block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $134. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STAT30 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, October 30.
If you make reservations after the cut-off date ask for the Statistical Horizon’s room rate (do not use the code) and they will try to accommodate your request.
- Review of linear regression
- Fixed and random effects
- Random intercepts model and intraclass correlation
- Mixed models (random intercepts models with covariates)
- Random coefficient models
- Model selection considerations
- Crossed random effects models
- Extensions to categorical and limited dependent variables
“Steve is the professor I wish I’d had in graduate school. He is a black belt at theory and technical details, and has the ability to communicate the materials in a way that helps you to grow an intuition. This is a rare quality in a statistician and teacher, and Steve nails it. He exhibits humor, thoughtful questions and responses, and the ability to anticipate where people get “stuck.” Take a course from Steve and you’ll be glad you did it!”
Andy Bogart, RAND Corporation
“Great course! Dr. Vaisey broke down complex concepts and made them very approachable, even for someone like myself with a limited statistics background.”
Bryn Mumma, University of California, Davis
“Stephen is an excellent instructor. He has clearly thought deeply about these types of models and is very knowledgeable about the methods. He was available for questions and willing to discuss student’s specific data with them. I am leaving this course with not only knowledge about how to analyze nested data, but also with knowledge about what methods are conventional for certain fields and what methods will be most recognized by others. I appreciate this meta-perspective. I would highly recommend this course for anyone who needs to gain a more thorough understanding of MLM and the differences between fixed and random effects.”
Kelsey Thiem, University of Massachusetts
“Stephen is an extremely effective and engaging teacher. The workshop was an excellent blend of technical issues and practical approaches to real world research problems.”
Jonathan Koltai, University of Toronto
“Stephen did an excellent job making difficult concepts easy to understand through examples and clear explanations. I learned how to better interpret, compare, and create practical models, all of which apply to many research projects with which I am involved.”
Scott Friedlander, Los Angeles Biomedical Research Institute