Selecting a Two-Group Classification Weighting Algorithm: Take Two

Abstract

The two-group cross-validation classification accuracies of six algorithms (i.e., least squares, ridge regression, principal components, a common factor method, equal weighting, and logistic regression) were compared as a function of degree of validity concentration, group separation, and number of subjects. Therein, the findings of two previous studies were extended to the latter three methods with particular interest in how logistic regression faired as a function of validity concentration. In respect to validity concentration, as well as group separation and N, logistic regression was a mirror image of least squares. The same relative decrease, in respect to alternate methods, in accuracy with increasing validity concentration previously evidenced with least squares was observed. However, the large number of samples in which logistic regression failed to yield a solution may be a cause for concern.