Why should we run the MANOVA test before discriminant analysis?
MANOVA can say how groups are significantly different i.e. how valid are the groups but Discriminant analysis can let us know how do groups differ i.e. which variables best distinguish among the groups. Discriminant Analysis operates on data sets for which pre-specified, well defined groups already exist.
What is the difference between MANOVA and discriminant analysis?
Hotelling’s T and MANOVA provide an overall test of the difference between the groups based on all of the numeric variables. The tests of significance are on these linear combinations rather than the original separate variables. Descriptive discriminant analysis is based on multivariate analysis of variance.
Which is the best post hoc analysis for a MANOVA?
I have studied Field’s “Discovering Statistics Using IBM SPSS Statistics” chapter 16, and he states that the preferred post-hoc analysis is a discriminant analysis, because of the linear combination in which the dependent variables are related to group membership in a MANOVA.
When to use MANOVA or univariate ANOVAs?
So the choice between these two follow-up approaches entirely depends on what you want to test. First, if you are “interested in how the three groups influence every dependent variable” (i.e. individual DVs are of primary interest), then you should arguably not run MANOVA at all, but go straight to univariate ANOVAs!
Which is a follow up test to MANOVA?
Linear Discriminant Analysis, LDA, (as a follow-up to MANOVA) aims at checking which linear combination of individual variables leads to maximal group separability and at interpreting this linear combination.
How to follow up a factorial MANOVA with discriminant analysis?
This question asked about one-way MANOVA with only a single factor, but see here for the [more complicated] case of factorial MANOVA: How to follow up a factorial MANOVA with discriminant analysis?