Structural Equation Modeling: Part 2
An On-Demand Seminar Taught by
Paul D. Allison, Ph.D.
To see a sample of the course slides, click here.
This seminar is Part 2 of a two-part sequence on SEM. Part 1 covers SEM basics. It will take place on July 12-August 9. For that seminar, you can find more information and register here.
For the last several years, Dr. Paul Allison has been teaching his acclaimed two- and five-day seminars on Structural Equation Modeling to audiences around the world. This Part 2 seminar covers advanced SEM topics, like instrumental variables, alternative estimation methods, multiple group models, models for binary and ordinal data, models for longitudinal data, and much more. To take Part 2, you should already have some knowledge of SEM, ideally by taking Part 1.
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 14 modules:
- Review of SEM
- Nonrecursive models
- Instrumental variables
- Known reliability for single indicators
- Second-order factor analysis
- Formative indicators
- Alternative estimation methods.
- Multiple group analysis
- Interactions with latent variables
- Models for ordinal and nominal data
- Missing data on binary variables
- Models for censored and event-time data
- Indirect effects in non-linear models
- Models for longitudinal data
The modules contain videos of the 4-day remote 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 is an assigned article 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:
- 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 THE 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
- 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.
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 SAS, Stata, Mplus 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 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?
Participants should have a good working knowledge of the basic principles of structural equation modeling. This requirement can be satisfied by taking Structural Equation Modeling: Part 1. It is also desirable that you be familiar with logistic regression (binary, ordinal, or nominal). To do the hands-on exercises, it is essential that you already be comfortable working with one of the four packages that will be covered in the seminar.
REVIEWS OF STRUCTURAL EQUATION MODELING
“I highly recommend this course to everyone who has a basic statistical background and would like to learn a new technique of data analysis. The course is well structured and well-paced. It is also quite comprehensive, as a lot of material (different kinds of models, etc.) is covered in a rather short amount of time. It’s very hands-on, as one can apply newly learned concepts in exercises and is able to ask questions as well as receive feedback. Also, the course reviews how to perform all analyses in different software and which ones are better for certain analyses. Overall, this is a great introductory course on SEM, very comprehensive. I highly recommend.”
Anna Sheremenko, ICF
“Professor Paul Allison is a wonderful teacher of SEM. It is my first time learning SEM and this course provides a full coverage of SEM topics. After this course, I feel more confident in handling SEM using different statistical softwares.”
Nicole W.T. Cheung, The Chinese University of Hong Kong
“The course content was very comprehensive, and Dr. Allison is an excellent instructor. I certainly recommend this course.”
Mariana Toniolo Barrios, Simon Fraser University
“The strength of this course is its focus on the practical implementation of structural equation modeling, particularly how to use it in statistical software packages. Dr. Allison is extremely knowledgeable in each and every program and provides excellent teaching and exercises to help you improve your own skills and understanding. Knowing how to use these techniques in software packages reinforces and strengthens your total understanding of the material.”
Kevin Baier, Westat
“Professor Allison is not only an extremely knowledgeable scholar on the subject matter, but also a highly effective instructor. He makes advanced data analysis and statistics easier for students of all levels to learn and master. I benefitted from his course on SEM tremendously and am actually looking forward to using SEM to answer some research questions that I previously couldn’t due to lack of confidence (even after taking 1 grad course on SEM and spending many hours self-learning). Thank you, Professor Allison.”
Yuning Wu, Wayne State University
“During this course was my first time using Mplus. I got a lot of practice and feel somewhat comfortable using it. It was also great that Dr. Allison welcomed all questions. Finally, the detailed notes and slides are great.”
“The course is taught so that people at various levels of understanding are able to understand the concepts. Furthermore, the exercises and activities allow opportunities to practice the techniques in class and after class. The course is also structured so that you can receive individual feedback if you desire.”
Juan Barthelemy, University of Houston
“I came into the program with bare minimum knowledge of structural equation modeling. At the end of the course, however, I am confident to say I have learned a lot in a short space of time. I am also excited now about using structural equation modeling and so it is definitely going to be an integral part of my research methods toolkit.”
Uchechi Anaduaka, Lingnan University