Re: some help with a covariate please

Matt <matt.lewis2@xxxxxxxxx> wrote in
news:60d40feb-246a-4370-b041-b3d46fdd90ad@xxxxxxxxxxxxxxxxxxxxxxxxxxx:

On Feb 1, 3:50 am, David Winsemius <doe_s...@xxxxxxxxxxx> wrote:
Matt <matt.lew...@xxxxxxxxx> wrote:
On Jan 30, 4:02 pm, David Winsemius  wrote:
Matt <matt.lew...@xxxxxxxxx> wrote :

It is a fairly restricted age range (11-18 and only 1 person
each is 11 and 18).  Total n is 85

Number of subjects or number of test points?

So a plot for age shows that the trend to improvement over
time is the same, but the form of this is different.

With the age range so curtailed like this is it more sensible
to use age as a between subjects factor in the repeated
measures analysis rather than a covariate?

What is important is that you accurately describe the
relationships in your data. You say that the "form of [trend to
improvement over time] is different". I think you would get more
sensible replies if you were both more expansive and more
specific about what you are seeing.

-- David Winsemius

the n refers to the number of subjects that completed the
assessment (which is actually 82 given exlusions)

all ages (have excluded the 11 and 18 year olds as they were n =
1, and there are no 17 year olds) show an improvement in
performance on this particular task

The data file is below of one of the examples

I'd love to post the graph but cannot unfortunately.

If a line was fit for each age, there would be improved
performance for each age.  Slopes would differ however.

It makes clear what you were saying about the age*repeat-test
effect. Older individuals learn to do the test faster over 5
repetitions than do younger individuals. Weren't you also dealing
with another covariate whose effect diminished when age was
entered?

--
David Winsemius

David

there has only been age used as a covariate. I'm sorry if i gave
the impression otherwise.

My query was more related to the fact that inclusion of age as a
covariate makes the main effect of time no longer significant.

I was curious to know if there is a way to examine this effect in
more depth, rather than provide a subjective description of the form
of the data.

I am not offering a "subjective description". I was talking about
observable patterns in your data. Your younger subjects only inmproved
from 1.84 to 1.61 while you oldest sujects improved from 1.86 to 1.38.
The question now comes up whether the measure variance and numbers of
sujects among the age groups (not yet specified) is sufficiently small
to allow a test of trend among the the youngest groups to give a
positive result when testing the hypothesis that the younger subjects
improved at all. That is probably why your sequence effect became non-
significant. Most of the variability I will bet that the p-value was
still suggestive though, perhaps around p=0.10. What was the trial
effect and p-value in your sequence*age model? Or even better, give us
all the data for the age=12 subgroup.

And while you are at it, you should probably give us the model formula
that you used.

--
David Winsemius
.

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