Confirmatory factor analysis (CFA) is based on the premise that observable variables are imperfect indicators of certain underlying, or latent, constructs. For example, variables used in the regression and path analytical models of Chapter 1, such as father’s education (FaEd), degree aspirations (Degre Asp), and highest held academic degree (Degree), can be thought of as imperfect indicators of the latent constructs parents’ socioeconomic status (PaSES), general academic motivation (AcMotiv), and one’s own socioeconomic status (SES), respectively. If more than one observed indicator variable is available to measure a particular latent construct, CFA allows the researcher to cluster these variables in prespecified, theory-driven ways to evaluate to what extent a particular data set “confirms” what is theoretically believed to be its underlying structure. Thus, the CFA approach to multivariate data analysis does not let a particular data set dictate, identify, or discover underlying dimensions [as is the case with other variable reduction techniques such as exploratory factor analysis (EFA) or principal components analysis (PCA)]; rather, it requires the researcher to theorize an underlying structure and assess whether the observed data “fits” this a priori specified model. In doing so, CFA provides a framework for addressing some of the problems associated with traditional ways of assessing a measure’s validity and reliability.