IWSM 2013   28th International Workshop on Statistical Modelling


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Short Course

Short Course will be held in "Edificio 19", first floor, room "aula multimediale C".

Due to limited number of seats in the laboratory room, the Short Course is reserved to 25 attendants.


An Introduction to Structural Equation Modelling with the sem Package for R

John Fox
Department of Sociology - McMaster University Hamilton, Ontario, Canada



Structural-equation models (SEMs) are multiple-equation regression models in which the response variable in one regression equation can appear as an explanatory variable in another equation. In "non-recursive" SEMs, two variables in a model can affect one-another reciprocally, either directly, or indirectly through a "feedback" loop. Structural-equation models can include "latent" variables -- variables that are not measured directly, but rather indirectly through their effects (called indicators) or, sometimes, through their observable causes.


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.


A sound background in single-equation regression models and some knowledge of basic matrix algebra are assumed, as is familiarity with basic statistical ideas such as the method of maximum likelihood.