Covariance structure analysis is a statistical technique in which a theoretical model, or a covariance structure, is constructed, and the covariances predicted by the theoretical model are compared with those of the observed data. The adequacy of the model in reproducing the sample covariances is reflected by estimates of the parameters of the model and measures indicating the goodness of fit. Covariance structure analysis is frequently used for analyzing data obtained in nonexperimental or quasiexperimental research, but is seldom employed in experimental research. In this paper, the applicability of this technique in experimental research is discussed and illustrated by covariance structure analysis studies in which two models for word translation—the symmetrical model and the asymmetrical model—are described, refined, and contrasted.