Yes, my question was about design. If you don't have a design, you don't have anything to talk about. If your method is "well established" and in use for decades, you should hardly have any question for us. If you can't follow their method, you must be
doing something different. They never reported Mauchly's test in a situation where it is impossible to compute (I hope).
Mauchly's is a warning that you should be careful about the tests. Well, with 72 periods of measure in an hour, you /should/ be careful about the tests, period.
But the tests that you should be concerned about are tests of hypothesis. I preferred using the old BDMP program (2V? 4V?) because it gave linear trends (say) with the test performed against the actual variation measured for the trend. (I don't know if
SPSS can do that testing, but it is not automatic.) Anyway, if you can't state the hypothesis, you can't get started.
If you expect an early response followed by a later decline (which could be reasonable for a brain-response to stimulus), the simplest execution might be to break the 72 periods into two or more sets: early response, middle, later. That is especially true
if the early response is very strong: Look for the early linear trend, then see if it continues or if the means regress back to the start.
For small and moderate samples, a non-significant Mauchly's test does not mean much at all. That is
why many people will recommend, wisely, that followup test be performed as paired t-tests instead of
using some pooled variance term.
What are you measuring? Is it a good measure, with good scaling expected and no outliers observed?
I don't like analyses where those corrections are made, unless I have a decent understanding of why
they are required, such as, the presence of excess zeroes.
Would some transformation be thought of, by anyone? Analyzing with unnecessarily-unequal variances
is a way to get into unneeded trouble. If the "levels" represent time, it might be appropriate and proper
to test a much more powerful hypothesis that makes use of contrasts (linear for growth, etc.) in order to
overcome the inevitable decline in correlations across time.
You say: more levels than subjects -- Is this because you have very small N or because you have moderate N
but also have too many levels to test a sensible hypothesis across them all?
State your hypotheses. What tests them? A single-d.f. test is what gives best power, whenever one of those
can be used. I favor constructing contrasts -- sometimes in the form of separate variables -- over tests that
include multiple d.f. and multiple hypotheses, all at once. And I would rather remove the causes of
heterogeneity (variances or correlations) beforehand, than have to hope that I have suitably corrected for it.
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