Discriminant Function Analysis
Overview
Discriminant function analysis, a.k.a. discriminant
analysis or DA, is used to classify cases into the
values of a categorical dependent, usually a
dichotomy. If discriminant function analysis is
effective for a set of data, the classification table of
correct and incorrect estimates will yield a high
percentage correct. Discriminant function analysis is
found in SPSS under Analyze, Classify,
Discriminant. One gets DA or MDA from this same
menu selection, depending on whether the specified
grouping variable has two or more categories.
Multiple discriminant analysis (MDA) is an
extension of discriminant analysis and a cousin of
multiple analysis of variance (MANOVA), sharing
many of the same assumptions and tests. MDA is
used to classify a categorical dependent which has
more than two categories, using as predictors a
number of interval or dummy independent variables.
MDA is sometimes also called
discriminant factor
analysis
or
canonical discriminant analysis
.
There are several purposes for DA and/or MDA:
To classify cases into groups using a
discriminant prediction equation.
To test theory by observing whether
cases are classified as predicted.
To investigate differences between or
among groups.
To determine the most parsimonious
way to distinguish among groups.
To determine the percent of variance
in the dependent variable explained
by the independents.
To determine the percent of variance
in the dependent variable explained
by the independents over and above
the variance accounted for by control
Contents
Key concepts
and terms
Tests of
significance
Effect size
measures
Interpreting
discriminant
functions
SPSS output
Assumptions
Frequently
asked
questions
Bibliography

variables, using sequential
discriminant analysis.
To assess the relative importance of
the independent variables in
classifying the dependent variable.
To discard variables which are little
related to group distinctions.
To infer the meaning of MDA
dimensions which distinguish groups,
based on discriminant loadings.
Discriminant analysis has two steps: (1) an F test
(Wilks' lambda) is used to test if the discriminant
model as a whole is significant, and (2) if the F test
shows significance, then the individual independent
variables are assessed to see which differ
significantly in mean by group and these are used to
classify the dependent variable.
Discriminant analysis shares all the usual
assumptions of correlation, requiring linear and
homoscedastic relationships, and untruncated
interval or near interval data. Like multiple
regression, it also assumes proper model
specification (inclusion of all important
independents and exclusion of extraneous
variables). DA also assumes the dependent variable
is a true dichotomy since data which are forced into
dichotomous coding are truncated, attenuating
correlation.

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- Spring '12
- rtgg
- Regression Analysis, Spss, Statistical significance, Multivariate statistics, discriminant function