Categorical Structural Equation Modeling
A 4-Day Livestream Seminar Taught by
Kevin Grimm10:30am-12:30pm ET (New York time)
1:30pm-3:00pm ET
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
The following statistical models will be discussed:
- Logistic and probit regression and path models
- Cumulative logit and probit regression and path models
- Confirmatory factor models for binary and ordinal indicators (e.g., 2-parameter logistic model, graded response model)
- Multiple group confirmatory factor models for binary and ordinal indicators
- Count outcomes in structural equation models
The following statistical models will be discussed:
- Logistic and probit regression and path models
- Cumulative logit and probit regression and path models
- Confirmatory factor models for binary and ordinal indicators (e.g., 2-parameter logistic model, graded response model)
- Multiple group confirmatory factor models for binary and ordinal indicators
- Count outcomes in structural equation models
Computing
Empirical examples and exercises will be demonstrated using Mplus and R. To participate in the hands-on exercises, you should have a computer with Mplus and/or R installed. All analyses can be completed using Mplus, while many (but not all) can be completed using R. R users should also install RStudio, a freely available interface for R, and the lavaan package for SEM. The demo version of Mplus will suffice.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Empirical examples and exercises will be demonstrated using Mplus and R. To participate in the hands-on exercises, you should have a computer with Mplus and/or R installed. All analyses can be completed using Mplus, while many (but not all) can be completed using R. R users should also install RStudio, a freely available interface for R, and the lavaan package for SEM. The demo version of Mplus will suffice.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Who should register?
If you want to learn how to apply structural equation models to binary and ordinal data and have a solid background in structural equation modeling, then this course is for you. Familiarity with either R or Mplus is strongly advised.
If you want to learn how to apply structural equation models to binary and ordinal data and have a solid background in structural equation modeling, then this course is for you. Familiarity with either R or Mplus is strongly advised.
Seminar outline
Path Analysis with Binary Outcomes
- Introduction to structural equation modeling
- Review of logistic and probit regression in R
- Introduction to Mplus and lavaan notation
- Logistic and probit regression in Mplus and lavaan
- Maximum likelihood estimation and weighted least squares estimation
- Path models with binary mediators and outcomes
Ordinal Outcomes & Confirmatory Factor Models
- Cumulative logit and probit regression models in R
- Cumulative logit and probit regression models in Mplus and lavaan
- Model fit for maximum likelihood and weighted least squares estimators
- Confirmatory factor models with binary indicators
- Confirmatory factor models with ordinal indicators
- Latent variable path models with binary and ordinal indicators
Multiple Group Confirmatory Factor Analysis, Missing Data, & Count Outcomes
- Multiple group confirmatory factor analysis with binary and ordinal indicators
- Missing data handling with maximum likelihood and weighted least squares estimators
- Review of count regression models in R
- Count regression models in Mplus
- Zero-inflated count regression models in Mplus
Path Analysis with Binary Outcomes
- Introduction to structural equation modeling
- Review of logistic and probit regression in R
- Introduction to Mplus and lavaan notation
- Logistic and probit regression in Mplus and lavaan
- Maximum likelihood estimation and weighted least squares estimation
- Path models with binary mediators and outcomes
Ordinal Outcomes & Confirmatory Factor Models
- Cumulative logit and probit regression models in R
- Cumulative logit and probit regression models in Mplus and lavaan
- Model fit for maximum likelihood and weighted least squares estimators
- Confirmatory factor models with binary indicators
- Confirmatory factor models with ordinal indicators
- Latent variable path models with binary and ordinal indicators
Multiple Group Confirmatory Factor Analysis, Missing Data, & Count Outcomes
- Multiple group confirmatory factor analysis with binary and ordinal indicators
- Missing data handling with maximum likelihood and weighted least squares estimators
- Review of count regression models in R
- Count regression models in Mplus
- Zero-inflated count regression models in Mplus
Payment information
The fee of $895 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $895 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.