Aims of the Workshop
This basic and brief introduction to SEMs takes up several topics: The form and specification of observed-variable SEMs; instrumental-variables (IV) estimation; determining whether or not an SEM, once specified, can be estimated (the "identification problem"); estimation of observed-variable SEMs by IV, two-stage least-squares, and full-information maximum-likelihood; general structural-equation models with latent variables, measurement errors, and multiple indicators. The sem package in R will be used to estimate structural-equation models.
BackgroundA sound background in single-equation regression models is assumed as is familiarity with basic statistical ideas, such as the method of maximum likelihood.
I also assume a basic knowledge of the R statistical computing environment. In addition to many books on R, there is a free introductory manual distributed with the software, as well as a variety of free contributed documentation.
Please make sure that R and the sem package are installed on your computer prior to the workshop.Resources
- lecture slides (to be posted prior to the workshop)
- R script for examples
- J. Fox, "Structural Equation Modeling in R with the sem Package" (An Appendix to An R Companion to Applied Regression, Second Edition,
by J. Fox and S. Weisberg, Sage 2011) - J. Fox, "Linear Structural-Equation Models" (Chapter 4, of Linear Statistical Models and Related Methods, Wiley, 1984)
Cost: McMaster, $30; Non-McMaster academic, $55, non-academic, $100.
For further information: contact John Fox, jfox@mcmaster.ca.
To register: contact Danielle Stayzer, stayzer@mcmaster.ca, 905-525-9140x24484.
Last Modified: 2013-11-06 by John Fox <jfox AT mcmaster.ca>


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