Re: Mixed 2-way ANOVA
- From: Richard Ulrich <Rich.Ulrich@xxxxxxxxxxx>
- Date: Wed, 28 Sep 2005 16:15:45 -0400
On 28 Sep 2005 03:50:14 -0700, "Corky" <corky.abc@xxxxxxxxxxxx> wrote:
> Hi,
>
> I am looking to carry out an experiment, i want to compare a control
> group made up of all particpants (to obtain a baseline) to 3 other
> groups (made up of all the particpants split into 3 groups -
> participants will be put into these groups depending on set criteria -
> the groups will probably be unequal). Each of the 4 groups will carry
> out 2 different tests and three measures will be collected.
I agree with Bruce's observation, that this seems to be 3 by
<something> instead of 4x2.
I want to further mention that when participants are "put into
these groups depending on set criteria", you apparently throw
away the chance of drawing the relatively-firm conclusions that
are possible for Randomized trials. Or did you intend some
other meaning?
In my consulting experience, a lot of people settled for two-group
designs, because it takes a lot of cases to get good power.
Even though there would be reasons why it would be neat to
compare 3 or 4 or 8 groups, it does take more cases.
If you have one-control versus many-trials, then your best
power is achieved by assigning extra cases to the Control.
But, of course, if you are not Randomizing, then you are
left with a contentious argument at the end, anyway, about
whatever you are testing.... One solution might be, call
it all "exploratory."
>
> So I will have a 4X2 mixed design. Can anybody help me out with sample
> sizes, i.e. how many participants will i need for each of the 4
> conditions in order for it to be statistically significant. I have read
> pages on power anylsis, but i t seems i need to carry out a pilot
> study, for which i don't have time. Any ideas?
One of the best guidelines, all the time, is to ask,
"How many cases are usually used for similar questions?"
- then, figure they probably did not use enough, and
add 50% or so.
If it is not randomized, you will want to see rather large
within-subject effects, even if the Ns can be large; since
you will have to contend that "other factors" could not have
been responsible for the outcome.
--
Rich Ulrich, wpilib@xxxxxxxx
http://www.pitt.edu/~wpilib/index.html
.
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