Structural Equation Modeling is a widely used technique – Randy
Schumacker's text on the subject has 1782 citations at Google
Scholar. Learn more from Prof. Schumacker himself, in his
online course,“Introduction to SEM,” November 12 – December 10
Nov 12: Categorical Data – Applied Modeling
Nov 12: Introduction to Structural Equation Modeling (more below)
Nov 19: Missing Data Analysis
Nov 26: Sample Size and Power Determination
Jan 14: Advanced Structured Equation Modeling
Structural Equation Modeling (SEM) is a general statistical
modeling technique to establish relationships among variables.
A key feature of SEM is that observed variables are understood
to represent a small number of "latent constructs" that cannot
be directly measured, only inferred from the observed measured
variables. This course covers the theory of SEM, and practical
work with computer software and real data. It covers the key
concepts in SEM - at the conclusion of the course students will
be able to specify different forms of models, using observed,
latent, dependent and independent variables. Student will be able
to conduct confirmatory factor analysis, and diagram SEM models.
The instructor, Dr. Randall E. Schumacker is Professor in
Educational Research at the University of Alabama. He is the
co-author of “A Beginner's Guide to Structural Equation Modeling”
(with Richard Lomax), “Advanced Structural Equation Modeling: New
Developments and Techniques” (with George Marcoulides) and the
co-editor (with George Marcolides) of “Advanced Structural
Equation Modeling: Issues and Techniques” and “Interaction and
Nonlinear Effects in Structural Equation Modeling”. Dr. Schumacker
was the founder, editor (1994-1998), and is the current emeritus
editor of “Structural Equation Modeling: A Multidisciplinary
Journal”. He also founded the Structural Equation Modeling Special
Interest Group at the American Educational Research Association.
Participants can ask questions and exchange comments with Dr.
Schumacker via a private discussion board throughout the period.
The course takes place online at statistics.com in a series of 4
weekly lessons and assignments, and requires about 15 hours/ week.
Participate at your own convenience; there are no set times when
you are required to be online.
You may leave the list at any time by sending the command
to [log in to unmask], leaving the subject line blank.