Structural Equation Modeling: Special Topics

A 3-Day Remote Seminar Taught by
Gregory R. Hancock, Ph.D.

Read reviews of this course

To see a sample of the course materials, click here.

Structural equation modeling (SEM) is a versatile analytical framework for estimating and assessing models that describe relations among both measured and latent variables. Common examples include measured variable path models, confirmatory factor models, and latent variable path models. These models subsume methods based on the traditional general linear model such as multiple regression and analysis of variance.

This seminar goes beyond introductory SEM to cover more advanced methods that enable researchers to address their current modeling questions more effectively and also to focus on entirely new research questions. After a review of SEM basics, we will cover

  • planned missing data designs
  • design-based approaches to complex samples
  • mean structure models for measured and latent variables
  • measured and latent variable mixture models
  • models for latent variable interactions
  • latent growth curve models
  • power analysis in SEM.

The style of instruction is designed for participants coming from a variety of different subject-matter backgrounds. Examples will be presented using the Mplus software package.

Starting May 13, we are offering this seminar as a 3-day synchronous*, remote workshop for the first time. Each day will consist of a 4-hour live lecture held via the free video-conferencing software Zoom. 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.

Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own. An additional lab session will be held Thursday and Friday afternoons, where you can review the exercise results with the instructor and ask any questions.

*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 two weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.


This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Before the seminar begins, you will receive an email with the meeting code and password you must use to join.  

Mplus will be used for all worked examples, but prior knowledge of Mplus is not essential. You will still greatly benefit from the instruction, comprehensive set of slides, and software syntax that you can apply later. If you wish to try the exercises, you should use a computer with the basic Mplus package installed (add-ons not needed).

WHO SHOULD Register?

The course will benefit applied researchers, analysts, and students interested in enhancing their understanding of SEM and developing their application skills. Participants are assumed to have been exposed to introductory SEM, such as that offered through an in-depth workshop or a typical university course, including such topics as measured variable path models, confirmatory factor models, latent variable path models, multigroup models, identification, estimation, fit, and SEM software implementation. An ideal preparation would be Paul Allison’s course Structural Equation Modeling: Part 1.


Day 1:

Review of introductory SEM

  • orientation/notation
  • data-model fit
  • measured variable path models
  • confirmatory factor models
  • latent variable path models
  • Mplus code / output
  • SEM resources

SEM for complex sample data

  • Design-based approaches
  • Model-based approaches
  • A peek at multilevel SEM

Missing data in SEM

  • Types of missing data and assumptions
  • Full-information maximum likelihood estimation
  • Auxiliary variables and the saturated correlates approach
  • Planned mixing data designs

Day 2:

Mean structure models

  • Measured mean structures
  • Latent mean structures for between-subjects designs
  • Structured means models for within-subjects designs
  • Means modeling extensions

Modeling latent variable interactions

  • Moderation
  • Unconstrained product-indicator approach
  • Latent moderated structural equations approach
  • Latent quadratic effects

Latent growth curve models

  • Brief review of longitudinal structural models
  • Foundations of latent growth modeling
  • Growth modeling extensions

Day 3:

Introduction to mixture models

  • Univariate mixtures
  • Multivariate mixtures / profile models
  • Regression mixtures
  • Confirmatory factor mixtures
  • Growth model mixture models

Power and sample size planning in SEM

  • Testing models as a whole
  • Testing parameters within a model
  • Planning for missing data

REVIEWS OF Structural Equation Modeling: Special Topics

“I learned more from this 2-day course than any other course on SEM I’ve ever taken.”
  Katie Darabos, City University of New York

“This course was helpful due to the instructor’s in-depth knowledge of SEM and the provision of example models with syntax. The instructor was great at showing the many possibilities of SEM. I left the course with new ideas for how I can analyze the data I have, as well as best methods for designing future studies.”
  Emily Cherenack, Duke University 

“This course included a great brief review of key SEM concepts and then offered many clear examples of more advanced applications. For 2 days, you get very good overviews and examples of how to assess group differences, moderation, mediation, latent growth modeling. I will be using these examples to guide my analyses and assist my students.”
  Shannon Lynch, Idaho State University

“I really enjoyed this course. Specifically, the experiential approach to learning advanced analyses within an SEM framework. I especially enjoyed the longitudinal modeling component surrounding how to conduct latent growth curve modeling. Dr. Hancock is very knowledgeable and presents the material in an accessible and compelling way. He answered questions and provided practical examples for each type of analysis. I really feel as though I learned a lot in a short amount of time. Highly recommend.”
  Colin Mahoney, Boston University

“This is a fantastic course for anyone who already has taken an introductory SEM workshop. The advanced topics helped me refine my SEM skills and added new analyses to my existing knowledge. The Mplus syntax and examples were helpful. Dr. Hancock is a talented instructor. This is a must take course.”
  Alan Goodboy, West Virginia University

“This is the most helpful course taught in the most helpful way to anyone interested in the topic.”
  Sigal Zilcha-Mano, Columbia University

“This was an excellent class. The instructor was outstanding. He made the material very digestible. A must for applied scientists without extensive SEM training.”
  Chrystal Vergara-Lopez, Brown University