> I am doing a small sample (N=7) crossover design project for a client

> where the influence of two drug treatments on 5 continous dv's are being

> assessed. I normally do large sample work and would use a repeated

> measures MANOVA or GLM for this type of problem, with bonferroni multiple

> comparisons. I am

> concerned about 1) violations of assumptions 2) skewness of variables with

> this sample size and 3) power if I use MANOVA/GLM for this analysis. Most

> of my nonparametric texts are designed for a single dv.

maximum achievement of a 5% test, referring to rank-order statistics.

That does not leave much scope for multiple variables, or for

Bonferroni adjustment for multiple tests.

b) There *exist* hardly any multivariate non-par tests, even if you

had a huge N.

PICK a single analysis.Quote:> I've looked

> through the published data in the area and multiple ANOVAs seem to be used

> a lot or several non-parametric tests on each dv. I would like to avoid

> inflating type 1 error by the latter choices. Is there an alternative to

> MANOVA or the set of several univariate analyses?

ESTABLISH one hypothesis, and one variable to measure the hypothesis.

Maybe you want to construct a composite variable to measure it.

If there were really huge effects, like the ones that will show up

with N=7, you really ought to have a pretty good idea what they are

from the uncontrolled observations that (ordinarily) precede a

designed study.

Of course, if you really don't know anything about the area you are

collecting data on, then you have to do the multiple tests and

proclaim the whole exercise to be Exploratory.

--

http://www.pitt.edu/~wpilib/index.html