One first performs the multivariate test, and, if statistically significant, proceeds to see which of the variables have significantly different means across the groups. Once group means are found to be statistically significant, classification of variables is undertaken. DA automatically determines some optimal combination of variables so that the first function provides the most overall discrimination between groups, the second provides second most, and so on. Moreover, the functions will be independent or orthogonal, that is, their contributions to the discrimination between groups will not overlap. Computationally, a canonical correlation analysis is performed that will determine the successive functions and canonical roots.
Classification is then possible from the canonical functions.
Subjects are classified in the groups in which they had the highest classification scores. The maximum number of discriminant functions will be equal to the degrees of freedom, or the number of variables in the analysis, whichever is smaller. Standardized coefficients and the structure matrix Discriminant functions are interpreted by means of standardized coefficients and the structure matrix. However, these coefficients do not tell us between which of the groups the respective functions discriminate. We can identify the nature of the discrimination for each discriminant function by looking at the means for the functions across groups.