SEM stands for “structural equation modeling,” and we are fortunate to have Prof. Randall Schumacker teaching this subject at Statistics.com.
Randy created the Structural Equation Modeling (SEM) journal in 1994 and the Structural Equation Modeling Special Interest Group (SIG) at the American Educational Research Association (AERA)
He has also co-authored several books, including: A Beginner’s Guide to Structural Equation Modeling (3rd Edition), Interaction and Non-linear Effects in Structural Equation Modeling, Advanced Structural Equation Modeling: Issues and Techniques,and Advanced Structural Equation Modeling: New Developments and Techniques.
SEM is a widely used method in educational and social science research where controlled randomized studies are rare, and researchers must work with observational data in its “native habitat.”
This can include multiple outcome variables, bi-directional causality, and hidden or latent variables that must be inferred from observed data. SEM’s function is to sort through these complexities and produce models.
Learn SEM with Prof. Schumacker as your guide, answering your questions and comments throughout the course on a private discussion forum.
This course covers the key concepts in SEM – at the conclusion of the course you will be able to specify different forms of models, using observed, latent, dependent and independent variables. You will also be able to conduct confirmatory factor analysis, and diagram SEM models. The software illustrated is primarily LISREL.
Prof. Randall Schumacker teaching this subject at Statistics.com.
This can include multiple outcome variables, bi-directional causality, and hidden or latent variables that must be inferred from observed data. SEM’s function is to sort through these complexities and produce models.