# Lecture 10 - Multivariate Analysis of Variance Flashcards

When do we use a MANOVA?

When there are two or more dependent variables

How does MANOVA work?

It creates a single new DV from the others. This is a linear combination of the experimental DV’s and it attempts to maximise the differences between treatment groups

What are three advantages of MANOVA?

Improved chance of finding out what changes occur due to the experimental treatment

Since all DVs are combined into one, it only counts as a single comparison and so type one error rate isn’t increased like it would be with multiple comparisons

It has more statistical power than ANOVA so it may show differences that an ANOVA cannot

What are the 5 assumptions of MANOVA

Multi variate normality

Homogeneity of variance-covariance matrices

Linearity

Multicolinearity

Singularity

How can we test for homogeneity of variance-covariance matrices in a MANOVA

We use Box’s M but with a criterion of p

What does multicolinearity mean?

The relationship between pairs of variables is high (r must be greater than 0.9)

What do we do if we find a significant multi variate effect

Then we conduct an ANOVA for each DV so we can look at univariate effects

If we find a significant univariate effect then we conduct post hoc tests where necessary

What are the assumptions of DFA

Same as a MANOVA

What does DFA stand for

Discriminant functions analysis

What does DFA do

It looks for a set of variables that can predict membership of groups

These predictors are normally chosen based on theories

DFA calculates different ‘functions’ that maximises the ability to predict group membership

What is the maximum number of functions in DFA

The number of levels of the grouping variable -1

Or

The number of degrees of freedom of the IV

What is a MANOVA used to work out

Can we predict group membership from a set of predictors? What proportion?

What are the differences between these predictors and how are they associated?