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Livestream Seminar

Categorical Structural Equation Modeling

A 4-Day Livestream Seminar Taught by

Kevin Grimm
Course Dates:

Monday, June 27, 2022 –
Thursday, June 30, 2022

 

Schedule:

10:30am-12:30pm ET (New York time): Live session via Zoom
1:30pm-3:00pm ET: Live session via Zoom

Watch Sample Video

Structural equation modeling (SEM) is a framework for fitting many types of statistical models, including simple regression models, multivariate path models, confirmatory factor models, latent variable path models, and latent growth models. Since its inception, the standard SEM has been a linear model with normally distributed outcomes. That’s been a big limitation because many outcome variables are binary or ordinal in almost every discipline.

While many SEM packages are still limited to linear/normal models, the last decade has seen the emergence of several SEM packages that do an excellent job of estimating non-normal models. Unfortunately, these models differ in several ways from standard SEM, and there is little didactic literature on how to properly use and interpret categorical SEM.

This seminar fills that gap by presenting a comprehensive treatment of SEM for binary and ordinal outcomes, using two of the best software packages for the task: Mplus and lavaan (a package for R).

Starting June 27, we are offering this seminar as a 4-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. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they are unable to attend at the scheduled time.

*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 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.

More details about the course content

Computing

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"I left the session feeling very energized and eager to apply what I'd learned to my own research.”

“The instructor was very clear and thorough, and he answered questions very well. I loved that I was going in expecting to learn certain material, and ended up learning that material thoroughly, but then also much more! I learned the answer to many questions I didn’t even know I had. I left the session feeling very energized and eager to apply what I’d learned to my own research.” 

Diane Holmberg

Acadia University

"Kevin went above and beyond explaining difficult concepts and he answered all questions posted."

“The course material was excellent. Kevin went above and beyond explaining difficult concepts and he answered all questions posted. Because Kevin is very knowledgeable about SEM, he can explain concepts simply. Really enjoyed the course. Thanks Kevin.” 

Cathy Drane

National Centre for Student Equity in Higher Education, Curtin University

“It was an excellent course with deep topic coverage..."

“It was an excellent course with deep topic coverage. I liked that Prof. Grimm explained topics clearly and broadly linked them to other statistical approaches (e.g. linkage between SEM and IRT). I also liked that codes were explained in two broadly used softwares: MPlus and the lavaan package for R, and advantages of each one were clearly compared.”

David Greger

Charles University, Prague

"This seminar provides an accessible guide to applying a variety of techniques..."

“Dr. Grimm is a talented instructor who does a great job of elucidating complicated concepts and clarifying exactly how Mplus and lavaan produce their estimation results. This seminar provides an accessible guide to applying a variety of techniques which I was able to put to immediate use in my work.”

Julien Leider

University of Illinois Chicago