Multilevel and Mixed Models Using Stata
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 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:
- 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 “between-within” model
- Know how to estimate these models and interpret the result
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 use Stata 16 for empirical examples and exercises. (Nearly all commands will work in Stata 14 or 15 as well.) However, no previous experience with Stata is needed. Participants who request it can also get the R notes and syntax as well.
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 includes all course materials. The early registration fee of $895 is available until March 17.
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 $164 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 STH416 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, March 16, 2020.
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.
- 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
“An excellent course. I felt like I went from no knowledge of mixed level models to feeling extremely competent at knowing when mixed level models may be used and how to use these models in just 2 days. Dr. Vaisey is an outstanding educator. This interactive, practical teaching style is extremely effective.”
Oluwaseun Falade-Nwulia, Johns Hopkins University
“Dr. Vaisey’s instruction helped me connect the dots of my previous understanding of relevant statistical methods. I feel more confident with applying what I learned to my future data analysis. Thank you. Dr. Vaisey!”
Yang Yang, Rowan University
“Dr. Vaisey uses techniques to teach MMMs that you cannot find in any single textbook and provides his recommended practices for model fit comparison (e.g. BIC and between within method) and why to use MMMs based on theoretically motivated questions.”
David Rothwell, Oregon State University
“This course was extremely helpful for reacquainting me with the mixed/multilevel model frameworks. It was not super heavy on formulas or syntax and focused mostly on grasping the basic understanding of partitioning variance and how to think about building a model that is most appropriate to a research question.”
Heidi Grunwald, Temple University
“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
“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