2016 Stata Summer School:
Multilevel and Mixed Models in Stata
Taught by Stephen Vaisey, Ph.D.
August 8-9, Hotel Birger Jarl Conference
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. We will then move on to mixed models (random effects models with fixed covariates) that allow us to ask how both individual-level and higher-level factors affect an outcome. Next, we will investigate how using random coefficients can help us model how individual-level processes work differently in different social contexts. Finally, we will use the example of hierarchical age-period-cohort models to explore how we can use crossed random effects to model more complicated forms of dependence.
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:
- Know the technical and substantive difference between fixed and random effects
- Understand what random intercept models, random coefficient models, and crossed random effects models are and when to use each one
- Know how to estimate and interpret these models in Stata
This seminar will use Stata 14 for the many empirical examples and exercises. However, no previous experience with Stata is assumed. To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer. If you do not already have Stata installed, a temporary license will be provided free of change. A power outlet and wireless access will be available at each seat.
WHO SHOULD ATTEND?
This course is for any who want to learn to apply multilevel models to observational data. Participants should have a basic foundation in linear regression.
FORMAT AND MATERIALS
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
Please go to the Metrika website for information on registration, and discounted hotel accommodations.
- 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
“This is a very practical and easy to digest course for people who are interested in moving into the area of matching and treatment effects. The code and course materials alone are worth the price, the excellent instructor is a bonus.”
Kathryn Nowotny, University of Colorado
“This course was very helpful in not only providing the information I needed to implement the methods, but also in explaining how to address challenges and how to write up and defend decisions.”
Liz Lawrence, University of Colorado
“This was my first time at a Statistical Horizons course, and I wanted to see how well these short courses could work for me as a way to address gaps. I found the course to be terrific, and stimulating, and plan to come to another.”
June Tester, UCSF Benioff Children’s Hospital
“Steve took a tough subject and broke it into smaller parts of the whole, which added great dimension to the subject. Thanks!”
Don Hunt, Georgia State University
“This is a fantastic class taught by a phenomenal instructor. I think any social scientist should take it.”
Gino Cattani, Stern School of Business, NYU
“It is a very informative, useful class on treatment effect.”
Pengxiang Li, University of Pennsylvania
“Excellent course, very easy to understand regardless of the domain you’re coming from.”
Razvan Lungeanu, Penn State University
“The clearest and most practical presentation of these diverse methods. I certainly feel confident to implement these methods.”
Lenny Lopez, Massachusetts General Hospital
“This course is very helpful. All the topics are based on real examples. It’s easy to follow up.”
Xinyan Yu, IMS Health
“I really enjoyed this course. It was easy to follow with many practical real world examples and clear theoretical bases behind the method.”
Bo Kyum Yang, University of Maryland
“Treatment Effects Analysis is the best course that I have taken within Statistical Horizons. The lecture and notes are clear, comprehensive and easily digested to be later implemented.”