 Given only two categories in the
dependent variable, both methods produce similar
results.
 Binary logistic regression
has one major advantage: it produces very helpful
plots.
 Discriminant analysis
offers a potential advantage: it classified ungrouped
cases.
 SPSS says: "The functions are
generated from a sample of cases for which group
membership is known; the functions can then be
applied to new cases with measurements for the
predictor variables but unknown group
membership."
 Depending on the research
problem, this may be more or less of an
advantage.
 Given more than two categories in the
dependent variable, binary logistic regression cannot be
use.
 Multinomial logistic
regression replaces binary logistic in this
situation.
 However, the multinomial logistic
analysis uses a different approach that does not
generate plots.
 This is where discriminant
analysis offers more advantages:
 It generates helpful plots,
especially a territorial map, to aid
analysis.
 It still offers the opportunity
for classifying cases that are "ungrouped" on the
dependent variable.
