Structural Equation Modeling: Part 1 - Online Course
A 4-day Livestream Seminar Taught by
Paul Allison11:00am-2:00pm EDT: Live lecture via Zoom
After 2:00pm EDT: Exercise assignment to be completed on your own
7:00pm-8:00pm EDT: Live “office hour” via Zoom to review exercises and ask questions
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 the basics of SEM from a master teacher, Professor Paul D. Allison, in just four days. This is an introductory course, and no previous knowledge of SEM is presumed.
Starting August 11, we are offering this seminar as a 4-day synchronous*, remote workshop. Each day will consist of a 3-hour, live morning lecture held via the free video-conferencing software Zoom. 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. Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own that afternoon. A final session will be held each evening as an “office hour”, where participants 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, meaning that you will get all of the class discussion and exercise solutions even if you cannot participate synchronously.
Computing
This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Participants will receive an email with information about joining the Zoom sessions prior to the course.
The empirical examples and exercises in this course will emphasize Mplus, but equivalent code will be presented for SAS, Stata and lavaan (a new 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 bring your own laptop 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.
This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Participants will receive an email with information about joining the Zoom sessions prior to the course.
The empirical examples and exercises in this course will emphasize Mplus, but equivalent code will be presented for SAS, Stata and lavaan (a new 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 bring your own laptop 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.
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 participants 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 participants 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.
Seminar outline
- Introduction to SEM
- Linear regression with missing data
- Path analysis of observed variables
- Direct and indirect effects
- Bootstrapping in SEM
- Reliability: parallel and tau-equivalent measures
- Multiple indicators of latent variables
- Heywood problems
- Exploratory factor analysis
- Confirmatory factor analysis
- Goodness of fit measures
- Structural relations among latent variables
- Introduction to SEM
- Linear regression with missing data
- Path analysis of observed variables
- Direct and indirect effects
- Bootstrapping in SEM
- Reliability: parallel and tau-equivalent measures
- Multiple indicators of latent variables
- Heywood problems
- Exploratory factor analysis
- Confirmatory factor analysis
- Goodness of fit measures
- Structural relations among latent variables
Payment information
The fee of $795 includes all course materials.
PayPal and all major credit cards are accepted.
All inquiries can be sent to info@statisticalhorizons.com.
Our Tax ID number is 26-4576270.
The fee of $795 includes all course materials.
PayPal and all major credit cards are accepted.
All inquiries can be sent to info@statisticalhorizons.com.
Our Tax ID number is 26-4576270.