Multilevel and Mixed Models Using Stata - Online Course
A 3-Day Livestream Seminar Taught by
Stephen Vaisey10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
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 October 11, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. 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.
*We understand that finding time to participate in livestream courses can be difficult. 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 four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
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 and random coefficients and 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.
Although these techniques apply to both nested and longitudinal data, in the interest of time we will focus exclusively on the nested data case. For courses focused on longitudinal data analysis, check out Longitudinal Data Analysis Using R or Longitudinal Data Analysis Using Stata.
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 and random coefficients and 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.
Although these techniques apply to both nested and longitudinal data, in the interest of time we will focus exclusively on the nested data case. For courses focused on longitudinal data analysis, check out Longitudinal Data Analysis Using R or Longitudinal Data Analysis Using Stata.
Computing
The vast majority of what you will learn in this course can be applied in any software package. This seminar will use Stata 17 for empirical examples and exercises. (Nearly all commands will work in Stata 14-16 as well.) However, no previous experience with Stata is needed.
Basic familiarity with Stata is highly desirable, but even novice Stata users should be able to follow the presentation and do the exercises.
R notes and syntax are available upon request.
If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.
The vast majority of what you will learn in this course can be applied in any software package. This seminar will use Stata 17 for empirical examples and exercises. (Nearly all commands will work in Stata 14-16 as well.) However, no previous experience with Stata is needed.
Basic familiarity with Stata is highly desirable, but even novice Stata users should be able to follow the presentation and do the exercises.
R notes and syntax are available upon request.
If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.
Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.
Who should register?
This course is for anyone who wants to learn to apply multilevel models to observational data. You should have a basic foundation in linear regression.
This course is for anyone who wants to learn to apply multilevel models to observational data. You should have a basic foundation in linear regression.
Seminar outline
Day 1:
- 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
Day 2:
- 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
Day 3:
- 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
Day 1:
- 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
Day 2:
- 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
Day 3:
- 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
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.