how to carry out multivariate analysis of variance

[#1]

I have several hundred Ss who fall into 1 of 6 groups based on residence (city, suburb, rural) and disability (yes, no). All Ss took a personality test of about 50 items, which are scored to yield 6 traits. Items are intermixed on the test to avoid order effects, but the traits may be intercorrelated. I want to analyze the 6 traits as dependent variables in a 3 (residence location) x 2 (disability status) analysis, with follow-up tests of significant effects as needed. In some stat programs this might be called multivariate analysis of variance, but how does JMP do such an analysis? Do I use the Fit Model platform or the Multivariate Methods platform, or something else?

[#2]

Go to 

     Help==>Statistics Index

Click on the list of statistics, and then start typing Multivariate or MANOVA, and the list of statistics will move down to where you will see multivariate analysis of variance

This will give you the start you need.

You can read up on this in the Fitting Linear Model's documentation

     Help==>JMP Documentation Library

[#3]

To look at the correlation (and scatter plots) of the dependent variables(y's):  Multivariate Methods>Multivariate add all of the y's.  Also test for outliers (test (Mahalanobis, Jackknife, et. al.) depends on the type of data the y's are). (You can also do the correlations by region or disability). You may then run fit model on the Y's of interest and add the two independent variables (residence and disability) as a Macro> factorial to degree.  This will give you analysis of variance and effects tables for the two factors and their interactions.  Make sure the two factors are type nominal when you run the fit model.