Multilevel and Mixed Models Using R

A 2-Day Seminar Taught by Stephen Vaisey, Ph.D.

Read reviews of this seminar

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

This course is currently full. If you would like to be added to the waitlist, please send us an email at ashley@statisticalhorizons.com.


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.

Computing

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 R for empirical examples and exercises. To replicate the instructor’s workflow in the course, come with R and RStudio installed on your computer. However, no previous experience with R is needed because all code will be provided. Although the course will be taught in R, complete Stata syntax for all analyses will be provided on request.

If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.


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 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 $995.00 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 block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $139 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STH728 or click here. For guaranteed rate and availability, you must reserve your room no later than Friday, June 28, 2019.

If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) and they will try to accommodate your request.


SEMINAR OUTLINE

  • What are multilevel models?
  • Hierarchical linear model motivation and notation
  • Within and between variance
  • Variance components models and plots
  • The first distinction between “random” and “fixed” effects
  • “Shrinkage” and empirical Bayes estimates of intercepts
  • What are mixed models a mix of?
  • Three types of R-squared
  • Random coefficients/slopes
  • Model selection with BIC and AIC
  • Basic diagnostics
  • The second distinction between “random” and “fixed” effects
  • When and why (not) to use random effects models
  • Testing the random effects assumption
  • The “between-within” method to combine the best of RE/FE
  • Centering and cross-level interactions
  • A brief comparison of clustered and panel data
  • Multilevel logistic regression and other limited dependent variables
  • Presenting results

RECENT COMMENTS FROM PARTICIPANTS

“This was one of the best workshops I have ever attended. The information was valuable, the teaching style was excellent, and the amount of time was just right. I wish I could learn all my statistics from the instructor. One day would have been too short. I gained a ton of insight into not just multilevel modeling, but model building in general. I would recommend it to any other fellows who are interested in this type of analyses. I would also say that I learned enough to be comfortable using it, not just as an ‘interesting technique.'”
  Raimee Eck, NIH

“Thanks to the extremely clear way Dr. Vaisey explained multilevel and mixed models and the efficiently prepared R codes, I came to a basic level understanding of these important techniques. They are very intuitive and applicable to many concepts we work with in the pharmaceutical industry. Thank you very much!”
  Defne Turker, Novo Nordisk Inc.

“The material is presented very well, with some humor to avoid tedious moments. The course is very good on model interpretation and the strategy of fitting successive models. The motivation behind model-fitting was delineated clearly throughout the course.”
  Terry Kissinger, Federal Deposit Insurance Corporation

“Very informative, well-paced, and easy to follow, with a good amount of exercises. Steve is an awesome teacher and engaging as well.”
  Anonymous

“Dr. Vaisey did an excellent job at incorporating both theory and practice into this seminar. I was originally skeptical about how much I would be able to take away from this seminar – but it was a lot! I’m confident I’ll be able to apply multilevel mixed models into my research.”
  James Khun, Fors Marsh Group