## small sample bistatistics analysis with 5 dependent vars

### small sample bistatistics analysis with 5 dependent vars

What is the best method for analysing a study in which 7 subjects are
exposed to 3 treatments and for which 5 continous dependent variables are
measured? The design is a crossover type for which my text recommends
several t-tests. Unfortunately the text procedures are for single
dependent variables. I would do a MANOVA if the sample size was larger but
due to the small N I am pretty sure the assumptions underlying MANOVA will
be violated. Plus power becomes a problem with this size N. I am not that
familiar with nonparametric stats but they seem to be directed at
experiments with one dependent variable. Can someone point me in the right
direction for a test where I can look at the 3 conditions effect of 5 dv's
with 7 subjects where I can have a hope of controlling type 1 error?

Thanks,
Tony

### small sample bistatistics analysis with 5 dependent vars

Your N is too small, even for non-parametric tests.

You want to estimate more parameters then you have data. This is not very
parsimonious.

Fedor Baart

### small sample bistatistics analysis with 5 dependent vars

Unfortunately, the client can't budget for more than this number of
participants as the project involves some very expensive bio-assays. They
are going ahead with N=6, I am just trying to find the best way to deal
with the data.
Thanks,
Tony

: Your N is too small, even for non-parametric tests.

: You want to estimate more parameters then you have data. This is not very
: parsimonious.

: Fedor Baart

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### small sample bistatistics analysis with 5 dependent vars

Take heed.  A sample size of 6 is pretty much meaningless.  What type of
conclusion can you reach from such a small sample size?

Dear newsgroup members,

We have a problem comparing the strength of regression coefficients of the
same independent variable in models with different dependent variables.
Below I will show two (related) examples:

Example 1.
Cross-sectional design. Dependent variables: two speed tasks (number of
seconds needed is recorded; the lower, the better, the faster) and tasks
differ in complexity/difficulty. Independent variable: age (0: young and 1:
old). Question: do older people particularly differ from younger people in

Example 2.
Cross-sectional design. Dependent variables: one speed task (number of
seconds needed is recorded; the lower, the better, the faster) and a memory
task (number of words reproduced after presentation; the higher, the better
(the memory)). Independent variable: age (0: young and 1: old). Question: do
older people particularly differ from younger people in the speed task (more
so than in the easy task)?

How can these effects of age be compared? In the first example both
dependent variables are in seconds, in the second the scales differ
completely. It seems to me that standardisation of at least the dependent
variable is needed? But how to test for a significant difference in the
effect of age.

Some have suggested MANOVA or GLM repeated measures designs. Is this the way
to go? And, foremost, should at least the dependent variable be standardised
then?

Thank you for any information.

Best regards,

Hans Bosma