Abstract
Simple modifications of principal component methods are described that have distinct advantages for structural analysis of relations among educational and psychological variables. Advantages include the provision for the incorporation of prior beliefs about errors in the variables, computational efficiency, tractability for large battery analysis, and the availability of hypothesis testing procedures. The methods are contrasted theoretically and empirically with conventional principal component methods and with maximum likelihood factor analysis.
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