> 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
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.