Multilevel/mixed models using Stata

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Multilevel/mixed models using Stata

Description

This course introduces multilevel/mixed modeling for nested and longitudinal data and its implementation in Stata. Mixed models contain both fixed effects, analogous to regression coefficients, and random effects, effects that vary across clusters. Participants will learn how to use mixed models to answer research questions about the observation- and cluster-level data and how to disaggregate these effects. Introductory theory, estimation, model building, and diagnostics will be discussed and demonstrated through many examples.

Are you more interested in panel-data models with unobserved individual-level heterogeneity, endogenous variables, or lagged variables? Check out our Panel-data analysis using Stata course.

The course will be interactive, use real data, and offer ample opportunity for specific research questions and for working exercises to reinforce what is learned.

Price: $1,395

We offer a 15% discount for group enrollments of three or more participants.

Course leader

Meghan Cain is the Assistant Director, Educational Services at StataCorp LLC. She earned her PhD in quantitative psychology from the University of Notre Dame, where her research focused on structural equation modeling, multilevel modeling, and Bayesian statistics. At Stata, she oversees the development of statistical trainings and webinars, creates videos for the Stata YouTube channel, and reviews Stata Press books.

The course was great—exactly what I was looking for. The instructor did an excellent job making difficult concepts easy to understand, showing us tricks in using Stata, and she also skillfully answered many difficult questions.

Dr. William Mangino
Associate Professor of Sociology
Hofstra University

Course topics

The nested data problem and potential solutions
The multilevel/mixed-effects model (MLMM) with mixed
- Theory and intuition
- Random-intercept models
- Random-coefficient (random-slope) models
- Estimation methods: maximum likelihood, restricted maximum likelihood, generalized least squares, and small-sample inference
- Model comparison: Wald test, likelihood-ratio test, information criteria
- Using residuals and diagnostic plots to check assumptions
Longitudinal data analysis
- Exploring and visualizing longitudinal data with the xt suite of commands
- Random-intercept models with xtreg
- Growth curve models with mixed
- Alternative covariance structures
More complex models
- Three-plus level models
- Crossed-effects models
- Binary and count responses with the me suite of commands
- Estimation via adaptive Gaussian quadrature
Report results from a multilevel modeling analysis

Prerequisite

Knowledge of linear regression and a working knowledge of Stata.

Next session

Course	Dates	Location	Cost	Enroll
Multilevel/mixed models using Stata	18–21 November 2024	Web based Web-based training offers the same great content as our classroom training. And just like our classroom training, web-based training is designed to be interactive. Watch and listen from your computer as the instructor teaches; follow along in your printed notes; ask the instructor questions; and work examples using Stata. The differences are that you attend web-based training courses in the comfort of your own home or office and that courses are divided into three- to four-hour sessions that take place over consecutive days.	$1,395	Registration closed.

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Notes

Enrollment is limited. This course may be offered in a classroom or web-based setting. The type (classroom or web-based) will be designated above when scheduled.

Classroom training courses are two-day courses that run from 8:30 a.m. to 4:30 p.m. each day. These courses take place at a training center where computers with Stata installed are provided. A continental breakfast, lunch, and an afternoon snack will also be provided.

Web-based training courses are four-day courses that run for three and a half hours each day. You will be provided with a temporary Stata license to install on your computer, a printed copy of the course notes, and all the course datasets so that you can easily follow along. Learn more about how our web-based training courses work, watch a video demonstration, and find technical requirements for participating in this type of training.

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