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| Duration: | 1 hour |
|---|---|
| Where: | Join us from anywhere! |
| Cost: | Free—but registrations are limited |
Data are often made up of groups—students in schools, individuals in companies, repeated measurements on individuals, and the like. Whether the groupings in your data arise in a nested fashion (students nested in schools and schools nested in districts) or in a nonnested fashion (regions crossed with occupations), you can fit a multilevel model to account for the lack of independence within these groups.
Join us as we introduce the concepts and jargon of multilevel modeling for nested and longitudinal data. We will demonstrate how to fit multilevel and longitudinal models using Stata's mixed command and how to visualize the results using Stata's predict, twoway, margins, and marginsplot commands. We will also include a brief introduction to other Stata commands that can be used to fit multilevel models for binary, categorical, count, and survival data as well as multilevel structural equation models (SEMs).
The webinar is free, but you must register to attend. Registrations are limited so register soon.
We will send you an email prior to the start with instructions on how to access the webinar.
Chuck Huber is Director of Statistical Outreach at StataCorp and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health and at the New York University School of Global Public Health. In addition to working with Stata's team of software developers, he produces instructional videos for the Stata YouTube channel, writes blog entries, develops online NetCourses, and gives talks about Stata at conferences and universities. Most of his current work is focused on statistical methods used by behavioral and health scientists. He has published in the areas of neurology, human and animal genetics, alcohol and drug abuse prevention, nutrition, and birth defects. Dr. Huber currently teaches survey sampling at NYU and introductory biostatistics at Texas A&M, where he previously taught categorical data analysis, survey data analysis, and statistical genetics.
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