Multilevel and Mixed Models Using Stata
A 4-Day Remote Seminar Taught by
Stephen Vaisey, Ph.D.
To see a sample of the course materials, click here.
This seminar provides an intensive introduction to multilevel models, 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.
Starting July 13, we are offering this seminar as a 4-day synchronous*, remote workshop. Each day will consist of a 3-hour live lecture held via the free video-conferencing software Zoom. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own. An additional lab session will be held Tuesday and Thursday afternoons, where you can review the exercise results with the instructor and ask any questions.
*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for two weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
MORE DETAILS ABOUT THE COURSE CONTENT
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
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 Register?
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.
– what are multilevel models?
– why use multilevel models?
– within and between unit variation
– hierarchical data-generating processes
– complete pooling, partial pooling, and no pooling
– intro to practice data: life satisfaction in Europe
– intraclass correlation and variance components
– estimation in Stata using -mixed-
– partial pooling, random effects, and empirical Bayes
– introduction to linear “mixed” models
– introduction to full -mixed- syntax
– within, between, and total R-squared
– interpreting model coefficients
– random coefficients/slopes
– model selection
– basic diagnostics
– random and fixed effect model assumptions
– linear fixed-effect (no pooling) models
– Hausman and other tests of assumptions
– between-within (BW) or “hybrid” model
– cross-level interactions
– limited dependent variables: binary and count
– workflow and presenting results
“Steve walked us through the rough terrain of MLMM with a smile on his face and mastery in his words and teachings. He has shown the pathway to MLMM proficiency and most importantly the ability to understand statistical concepts and statistical intuition that go above and beyond. I bet we all are going to succeed in getting more and better MLMM research done. Thanks Steve.”
Jose Ferreira Pinto, University of Macau
“Multilevel modeling has always seemed like a complex topic made difficult to grasp by poorly written texts chock full of complex systems of equations. Through a ground up approach, plain language instruction, and comprehensive examples, Statistical Horizons has developed an excellent introduction to MLM and I now feel well equipped to use this tool in my own work.”
Tyler Varisco, University of Houston
“If you want to learn (not memorize) multilevel models, when and why you would use them, and then how to use them, this is your course. Stephen is an enthusiastic and down to earth teacher. Impressive ability to make complicated issues, or so I was taught, into very useful and interesting models. Fantastic instructor!”
Robert J. Eger III, Graduate School of Defense Management
“I greatly appreciated the insight and wisdom of thinking completely through the reasons and research questions that are motivating me to use MLM. I think this mental planning is 80% of the challenge.”
Robert Stoddard, Software Engineering Institute/Carnegie Mellon University