Structural Equation Modeling (SEM) is a popular method to test hypothetical relationships between constructs in the social sciences. These constructs may be unobserved (a.k.a., “latent”) or observed (a.k.a., “manifest”).
In this webinar, guest instructor Manolo Romero Escobar will describe the different types of SEM: confirmatory factor analysis, path analysis for manifest and latent variables, and latent growth modeling (i.e., the application of SEM on longitudinal data).
We’ll discuss the different terminology, the commonly used symbology, and the different ways a model can be specified, as well as how to present results and evaluate the fit of the models.
This webinar will be at a very basic conceptual level; however, it is assumed that participants have an understanding of multiple regression, interpretation of statistical tests, and methods of data screening.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Before working as a psychometrician, he worked extensively as a research and statistical consultant for faculty members, students at York University, and a variety of clients including health researchers, imaging clinics, educational institutions, and the Ontario government.
He has extensive expertise in factor analytical and latent-trait methods of measurement, as well as applications of linear mixed effects to nested, longitudinal, unbalanced data.
Manolo is passionate about the implementation of technology as an educational, learning, and training tool. He is an Excel, SPSS, and Mplus power user, and a supporter of the expanding use of the R language and environment for statistical computing.
You'll get access to this training webinar, 100+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.