title: Introduction to Structural Equation Modelling Using SPSS and Amos
author:  Niels J. Blunch 
description: Blunch has produced a useful addition to the catalogue of introductory SEM textbooks...He differentiates his texts from others by orienting all of his examples around a single SEM software package, by almost exclusively using the programming interface of AMOS, and by including a chapter on PCA and EFA - <br /><b> The American Statistician <b></b></b>
zlib_text: By Hui Bian 
  Office For Faculty Excellence 
  Fall 2011 
  1 
  
   Regression models:  
   only observed variables are modeled. 
   only the dependent variable in regression has an error term. Independent 
  variables are assumed to be modeled without error.  
   The partial coefficient for any independent variable controls for all other 
  independents, whether or not an actual causal effect is plausible.  
   Path models:  
   only observed variables without latent variables.  
   Unlike regression models but like structural equation models, independents 
  can be both causes and effects of other variables.  
   Only the endogenous variables in path models have error terms. Exogenous 
  variables in path models are assumed to be measured without error.  
   Partial coefficients are calculated using only the independents in a direct path 
  to the endogenous variable.  
  2 
  
   AMOS output 
   Standardized regression weights:  
   Structural or path coefficients in SEM. Standardized 
  estimates are used, for inst