Assumptions underlying discriminant analysis
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.
Note
IBM SPSS Statistics gives you statistical and graphical tools to assess the normality assumption. See Chapter 4 for a way to assess multivariate normality. Box's M test is available as part of the Discriminant procedure...
