When using discriminant analysis, you make the following assumptions:
- Independence of the observations. This rules out correlated data such as multilevel data, repeated measures data, or matched pairs data.
- Multivariate normality within groups. Strictly speaking, the presence of any categorical inputs can make this assumption untenable. Nonetheless, discriminant analysis can be robust to violations of this assumption.
- Homogeneity of covariances across groups. You can assess this assumption using the Box's M test.
- Absence of perfect multicollinearity. A given input cannot be perfectly predicted by a combination of other inputs also in the model.
- The number of cases within each group must be larger than the number of input variables.
IBM SPSS Statistics gives you statistical and graphical tools to assess the normality assumption...
