Structural Equation Modeling: A
A 2-Day Seminar Taught by Gregory Hancock, Ph.D.
Structural equation modeling (SEM) is a versatile analytical framework for estimating and assessing models that describe relations among both measured and latent variables. Common examples include measured variable path models, confirmatory factor models, and latent variable path models. These models subsume methods based on the traditional general linear model such as multiple regression and analysis of variance.
This seminar goes beyond introductory SEM to cover more advanced methods that enable researchers to address their current modeling questions more effectively and also to focus on entirely new research questions. After a review of SEM basics, we will cover real data challenges (e.g., missing data, nonnormality, complex samples, categorical data), mean structure models for measured and latent variables, models for latent variable interactions, latent growth curve models, and power analysis in SEM. The style of instruction is designed for participants with a variety of content backgrounds. Examples will be presented using the Mplus software package.
Mplus will be used for all worked examples, but prior knowledge of Mplus is not essential. Participants are welcome and encouraged to bring their own laptop computer with the basic Mplus package installed, which will be used for hands-on exercises during the last hour of each day for those interested participants. Doing so is not required, however. Participants will still greatly benefit from the instruction, comprehensive set of slides, and software syntax that they can apply at home.
WHO SHOULD ATTEND?
The course will benefit applied researchers, analysts, and students interested in enhancing their understanding of SEM and developing their application skills. Participants are assumed to have been exposed to introductory SEM, such as that offered through an in-depth workshop or a typical university course, including such topics as measured variable path models, confirmatory factor models, latent variable path models, multigroup models, identification, estimation, fit, and SEM software implementation. An ideal preparation would be Paul Allison’s 2-day course Introduction to Structural Equation Modeling.
LOCATION, Format AND MATERIALS
The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
REGISTRATION AND LODGING
The fee of $995.00 includes all seminar materials. The early registration fee of $895.00 is available until April 16.
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
Lodging Reservation Instructions
Hotel information will be posted when available.
- Review of SEM and software basics
- Real data challenges
- missing data
- complex samples
- categorical data
- Mean structure models for measured and latent variables
- Latent variable interactions
- Latent growth curve models
- Power analysis in SEM
“The content of this course was very applicable to my current work. I greatly appreciated the real-world in class exercises and examples. These helped me envision how to work with my own data. Greg Hancock is an outstanding instructor who can explain complex topics to students with more and less sophisticated backgrounds. He also uses humor and personal anecdotes very effectively.”
Tony Perez, Old Dominion University
“The facilitator, Dr. Greg Hancock, struck the right balance between explaining the concepts behind the methods and getting participants exposed to technical application of the methods. The facilitator is a gifted instructor and a humorous person, which kept the classes interesting.”
Anthony Waddimba, Bassett Research Institute
“Greg is an excellent instructor. He brought the material alive with his real world examples and personal humor. He presented SEM models that I had previously believed were not possible in a concise manner. I highly recommend this course for anyone whose work deals with latent variable analysis.”
Gerald Arnold, American Board of Internal Medicine (ABIM)
“As a natural scientist I really enjoyed learning methods and techniques from the social sciences, and how they could be applied to my field.”
Kris Johnson, US Forest Service
“Greg Hancock is a wonderful instructor and if you are interested in advanced SEM techniques, I highly recommend this course!! Beyond being an expert in the field, his engaging pedagogy and genuine interest in your research and activities will make this course well worth your time and money.”
John Barnshaw, American Association of University Professors
“Dr. Hancock is very knowledgeable about the subject matter. He explains complex stat terms well. He is also attentive to students’ questions and helpful with inquiries.”
Billy Bai, University of Nevada, Las Vegas