Structural Equation Modeling: Part 1 - Online Course
A 4-Week On-Demand Seminar Taught by
Paul AllisonMonday, June 16 —
Monday, July 14, 2025
Each Monday you will receive an email with instructions for the following week.
All course materials are available 24 hours a day. Materials will be accessible for an additional 2 weeks after the official close on July 14.
This seminar is Part 1 of a two-part sequence on SEM. Part 2 covers more advanced SEM topics, like instrumental variables, alternative estimation methods, multiple group models, models for binary and ordinal data, and models for longitudinal data. Part 2 will be offered July 21-August 18.
For the last several years, Dr. Paul Allison has been teaching his acclaimed short seminars on Structural Equation Modeling to audiences around the world. This seminar covers structural equation modeling (SEM) basics. It is is an introductory course, and no previous knowledge of SEM is presumed.
The course takes place in a series of four weekly installments of videos, quizzes, readings, and assignments, and requires about 6-8 hours/week. You can participate at your own convenience; there are no set times when you are required to be online. The course can be accessed with any recent web browser on almost any platform, including iPhone, iPad, and Android devices. It consists of 12 modules:
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- Introduction and linear regression
- FIML for missing data, path analysis
- Direct and indirect effects
- Indirect effects
- Bootstrapping and partial correlations
- Latent variable models and classical test theory
- Parallel, tau-equivalent and congeneric measures
- Confirmatory factor analysis
- Maximum likelihood estimation
- Goodness of fit
- Modification indices, correlated errors, the general structural equation model
- Concluding thoughts and advice
The modules contain videos of the 4-day livestream version of the course in its entirety. Each module is followed by a short multiple-choice quiz to test your knowledge. There are also weekly exercises that ask you to apply what you’ve learned to a real data set.
Each week, there are 2-3 assigned articles to read. There is also an online discussion forum where you can post questions or comments about any aspect of the course. All questions will be promptly answered by Dr. Allison.
Downloadable course materials include the following PDF files:
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- All slides displayed in the videos.
- Exercises for each week.
- Readings for each week.
- Computer code for all exercises (in Mplus, SAS, Stata, and R formats).
- A certificate of completion.
More details about course content
Structural Equation Modeling (SEM) is a statistical methodology that is widely used by researchers in the social, behavioral and educational sciences. First introduced in the 1970s, SEM is a marriage of psychometrics and econometrics. On the psychometric side, SEM allows for latent variables with multiple indicators. On the econometric side, SEM allows for multiple equations, possibly with feedback loops. In today’s SEM software, the models are so general that they encompass most of the statistical methods that are currently used in the social and behavioral sciences.
Here Are a Few Things You Can Do With Structural Equation Modeling
-
- Test the implications of causal theories.
- Estimate simultaneous equations with reciprocal effects.
- Incorporate latent variables with multiple indicators.
- Investigate mediation and moderation in a systematic way.
- Handle missing data by maximum likelihood (better than multiple imputation).
- Adjust for measurement error in predictor variables.
- Estimate and compare models across multiple groups of individuals.
- Represent causal theories with rigorous diagrams.
- Investigate the properties of multiple-item scales.
Because SEM is such a complex and wide-ranging methodology, learning how to use it can take a substantial investment of time and effort. Now, you have the opportunity to learn SEM from a master teacher, Professor Paul D. Allison, in just four weeks.
Structural Equation Modeling (SEM) is a statistical methodology that is widely used by researchers in the social, behavioral and educational sciences. First introduced in the 1970s, SEM is a marriage of psychometrics and econometrics. On the psychometric side, SEM allows for latent variables with multiple indicators. On the econometric side, SEM allows for multiple equations, possibly with feedback loops. In today’s SEM software, the models are so general that they encompass most of the statistical methods that are currently used in the social and behavioral sciences.
Here Are a Few Things You Can Do With Structural Equation Modeling
-
- Test the implications of causal theories.
- Estimate simultaneous equations with reciprocal effects.
- Incorporate latent variables with multiple indicators.
- Investigate mediation and moderation in a systematic way.
- Handle missing data by maximum likelihood (better than multiple imputation).
- Adjust for measurement error in predictor variables.
- Estimate and compare models across multiple groups of individuals.
- Represent causal theories with rigorous diagrams.
- Investigate the properties of multiple-item scales.
Because SEM is such a complex and wide-ranging methodology, learning how to use it can take a substantial investment of time and effort. Now, you have the opportunity to learn SEM from a master teacher, Professor Paul D. Allison, in just four weeks.
Computing
The empirical examples and exercises in this course will emphasize Mplus, but equivalent code will be presented for SAS, Stata, and lavaan (a package for R). Mplus is one of the best SEM packages because of its superior capabilities for missing data, multi-level modeling, and ordinal and categorical data.
To fully benefit from the course, you should use your own computer loaded with a recent version of Mplus, SAS, Stata, or R (with the lavaan package installed). Whichever package you choose, you should already be familiar with basic data management operations and the commands/procedures for doing linear regression, logistic regression, etc.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
There is now a free version of SAS, called SAS OnDemand for Academics, that is available to anyone.
If you’d like to use Stata for this course but don’t yet have much experience with that package, we recommend following along with a “getting started” video like the one here.
If you are not yet ready to purchase Stata, you can take advantage of StataCorp’s 30-day software return policy.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.
The empirical examples and exercises in this course will emphasize Mplus, but equivalent code will be presented for SAS, Stata, and lavaan (a package for R). Mplus is one of the best SEM packages because of its superior capabilities for missing data, multi-level modeling, and ordinal and categorical data.
To fully benefit from the course, you should use your own computer loaded with a recent version of Mplus, SAS, Stata, or R (with the lavaan package installed). Whichever package you choose, you should already be familiar with basic data management operations and the commands/procedures for doing linear regression, logistic regression, etc.
If you’d like to familiarize yourself with Mplus basics before the seminar begins, we recommend reading through UCLA’s short guide here.
There is now a free version of SAS, called SAS OnDemand for Academics, that is available to anyone.
If you’d like to use Stata for this course but don’t yet have much experience with that package, we recommend following along with a “getting started” video like the one here.
If you are not yet ready to purchase Stata, you can take advantage of StataCorp’s 30-day software return policy.
If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.
Who should register?
This course is designed for researchers with a moderate statistical background who want to apply SEM methods in their own research projects. No previous background in SEM is necessary. But you should have a good 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 with a moderate statistical background who want to apply SEM methods in their own research projects. No previous background in SEM is necessary. But you should have a good 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.
Registration instructions
The fee of $695 (USD) includes all course materials. All major credit cards are accepted.
This course is hosted on a platform called DigitalChalk. To register, you’ll need to go to statisticalhorizons.digitalchalk.com and click on Create Account. Then you will enter your name and email address, and create a password. Be sure to save your password because you will need it to logon to the course itself.
When you have created your account, you’ll be taken to your new home page. Click on the Register Now button (or click the Catalog icon on the left-hand column), and you’ll see “Structural Equation Modeling: Part 1” as one of the available courses. At the bottom of the box for that course, click the green button Add to Cart. Next click the green button at the top that says Checkout. You will then be prompted for your credit card information.
When you have finished the payment process, you will be taken back to your home page. Click on Dashboard to see Structural Equation Modeling: Part 1. When the course begins on June 16, you can click the play button to get started.
The fee of $695 (USD) includes all course materials. All major credit cards are accepted.
This course is hosted on a platform called DigitalChalk. To register, you’ll need to go to statisticalhorizons.digitalchalk.com and click on Create Account. Then you will enter your name and email address, and create a password. Be sure to save your password because you will need it to logon to the course itself.
When you have created your account, you’ll be taken to your new home page. Click on the Register Now button (or click the Catalog icon on the left-hand column), and you’ll see “Structural Equation Modeling: Part 1” as one of the available courses. At the bottom of the box for that course, click the green button Add to Cart. Next click the green button at the top that says Checkout. You will then be prompted for your credit card information.
When you have finished the payment process, you will be taken back to your home page. Click on Dashboard to see Structural Equation Modeling: Part 1. When the course begins on June 16, you can click the play button to get started.