Introduction to Structural Equation Modeling - Online Course
A 3-Day Livestream Seminar Taught by
Chris Johnston10:00am-12:30pm (convert to your local time) Thursday-Saturday
1:30pm-4:00pm Thursday, 1:30pm-3:30pm Friday & Saturday
Structural Equation Modeling (SEM) is a general framework for statistical analysis. What distinguishes SEM is that it allows for multiple equations that include latent variables measured with multiple indicators. This allows the researcher to adjust for measurement error and examine the properties of instruments, while simultaneously estimating structural relationships among variables, including direct and indirect effects. This course will provide an intensive introduction to the fundamentals of SEM, as well as several intermediate and advanced topics. Participants will receive guidance on how to implement SEM in widely used software packages.
Starting December 15, we are offering this seminar as a 3-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. 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.
*We understand that finding time to participate in livestream courses can be difficult. 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
This seminar provides an intensive introduction to the fundamentals of Structural Equation Modeling (SEM). The course will be roughly divided into four parts.
In Part 1, we will begin with a brief introduction to the SEM framework and what distinguishes SEM from other approaches to statistical analysis. We will then consider maximum likelihood estimation of the simple linear regression model within an SEM framework with both complete and missing data.
In Part 2, we will consider models with multiple, simultaneous equations. We will first explore simple path analysis, including the estimation of direct and indirect effects. We will then consider issues of identification that arise with more complex models, such as those with reciprocal effects.
In Part 3, we will turn to issues of measurement. We will begin by considering the concepts of reliability and validity and the advantages of multiple indicators when measuring a latent construct (e.g., capacity to specify and attenuate measurement error). We will then consider confirmatory factor analysis (CFA), including estimation, interpretation, and assessment.
In Part 4, we will explore a few advanced topics. First, we will integrate the measurement models of Part 3 with the structural models of Part 2 within a general SEM approach. We will then consider alternative methods of estimating these models beyond maximum likelihood. Finally, we will look at estimation with categorical observed variables.
Here are some of the things you will be able to do by the end of this course:
-
- Handle missing data using full information maximum likelihood.
- Investigate mediation in SEM models.
- Investigate the properties of multi-item scales with confirmatory factor analyses (CFA).
- Incorporate latent variables into your regression models.
- Use different SEM estimation methods to address continuous and categorical observed variables.
This seminar provides an intensive introduction to the fundamentals of Structural Equation Modeling (SEM). The course will be roughly divided into four parts.
In Part 1, we will begin with a brief introduction to the SEM framework and what distinguishes SEM from other approaches to statistical analysis. We will then consider maximum likelihood estimation of the simple linear regression model within an SEM framework with both complete and missing data.
In Part 2, we will consider models with multiple, simultaneous equations. We will first explore simple path analysis, including the estimation of direct and indirect effects. We will then consider issues of identification that arise with more complex models, such as those with reciprocal effects.
In Part 3, we will turn to issues of measurement. We will begin by considering the concepts of reliability and validity and the advantages of multiple indicators when measuring a latent construct (e.g., capacity to specify and attenuate measurement error). We will then consider confirmatory factor analysis (CFA), including estimation, interpretation, and assessment.
In Part 4, we will explore a few advanced topics. First, we will integrate the measurement models of Part 3 with the structural models of Part 2 within a general SEM approach. We will then consider alternative methods of estimating these models beyond maximum likelihood. Finally, we will look at estimation with categorical observed variables.
Here are some of the things you will be able to do by the end of this course:
-
- Handle missing data using full information maximum likelihood.
- Investigate mediation in SEM models.
- Investigate the properties of multi-item scales with confirmatory factor analyses (CFA).
- Incorporate latent variables into your regression models.
- Use different SEM estimation methods to address continuous and categorical observed variables.
Computing
There are several excellent software packages available for Structural Equation Modeling. This seminar will primarily use the freely-available R software and the R package ‘lavaan’ for in-class examples and exercises. No previous experience with R is needed for this course, as all code will be provided. While the focus will be on R, code will also be provided in Stata and Mplus.
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.
There are several excellent software packages available for Structural Equation Modeling. This seminar will primarily use the freely-available R software and the R package ‘lavaan’ for in-class examples and exercises. No previous experience with R is needed for this course, as all code will be provided. While the focus will be on R, code will also be provided in Stata and Mplus.
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?
This course is designed for researchers who want to apply SEM methods in their own research projects. No previous background in SEM is necessary. But participants should have a working knowledge of basic principles of statistical inference (e.g., standard errors, hypothesis tests, confidence intervals), and should also have a good understanding of the basic theory and practice of linear regression.
This course is designed for researchers who want to apply SEM methods in their own research projects. No previous background in SEM is necessary. But participants should have a working knowledge of basic principles of statistical inference (e.g., standard errors, hypothesis tests, confidence intervals), and should also have a good understanding of the basic theory and practice of linear regression.
Seminar outline
- Introduction to SEM
- Linear regression with complete and missing data
- Path analysis
- Direct and indirect effects
- Issues of identification
- Reliability and validity
- Confirmatory factor analysis
- Assessing goodness of fit
- The general SEM model
- Estimation beyond maximum likelihood
- Models for categorical observed variables
- Introduction to SEM
- Linear regression with complete and missing data
- Path analysis
- Direct and indirect effects
- Issues of identification
- Reliability and validity
- Confirmatory factor analysis
- Assessing goodness of fit
- The general SEM model
- Estimation beyond maximum likelihood
- Models for categorical observed variables
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.