Although these descriptions suggest that discriminant analysis would produce very different results from logistic regression, the main results are very similar, if not identical, in standard applications.

• Consider the simplest case:
  • A dichotomous dependent variable: vote for Bush or Clinton in 1992
  • A single independent variable: party identification
  • Studying a 7% sample of about 150 cases from the 1992 NES survey.
  • Here's the distribution for 87 respondents who reported voting for Clinton or Bush:

Binary logistic regression in SPSS has many options. The analysis below was run

--treating Party ID by default as a covariate (interval scale), not opting to treat it as categorical (discrete)

--requesting a classification table and plot

Discriminant Analysis in SPSS also has many options. This analysis was run

--treating Party ID as a factor (either interval or discrete); there is no option

--requesting function coefficients

--requesting a summary (classification) table

--requesting probabilities equal to group proportions

• The overall classification results for the two methods are identical
  • the equations differ because the dependent variables are scaled differently
  • in this instance, the plot from binary logistic analysis is unique

Binary Logistic regression produces a plot: