Factor analysis has historically been used for myriad purposes in the social and behavioral sciences, but an especially important application of this technique has been to evaluate construct validity. Since in the present milieu both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are readily available to the researcher, it is interesting to note several differences in the analytic traditions between the two techniques, even though they are both part of a single general linear model. This paper presents heuristic data to compare these two analytic methods, in addition to discussing recent published exemplars of both practices. The methods are compared with a dataset from K. Holzinger and F. Swineford (1939). EFA is a data reduction technique that permits the reduction of a large number of variables into constituent components by examining the amount of variance that can be reproduced by the latent or synthetic variables underlying the observed or measured variables. The development of true CFA has allowed researchers to test directly the fit between theories and data structure with a sample of data in hand rather than allowing a factor structure to emerge from data without regard to theoretical expectations as in exploratory factor analysis. Both techniques allow researchers to develop and critically examine theories regarding the structure of data sets. (Contains 16 tables and 49 references.) (Author/SLD)