Dear Michael,
> my design is built like this-
> participants see stimuli which can be from 6 relevant conditions
> from time to time a catch question appears
> so in total i have 7 conditions- 6 experimental conditions and 1 irrelevant "catch" condition
>
> for each of the 6 relevant stimuli types (but not for the 7th condition), i am measuring the response times.
> i need to covary this out since it differs significantly from one condition to another.
>
> how can i achieve this?
This is a tricky issue. One way to do this might be to define all of
your 6 event types as a single condition, and then add parametric
modulators that code for the 6 conditions (e.g. how each condition
differ from the mean) and response time. This will help remove the
variance associated with response time from the error (i.e.
unexplained bits) of your data. To contrast conditions, you could
contrast across the parametric modulators coding for each condition.
Though parametric modulators are serially orthogonalized, so the order
in which you enter things may make a difference (if they are not
completely independent).
Also, perhaps more importantly, if your conditions differ
significantly in response time, there isn't really a good way to
"covary out" response time. This is discussed by this very helpful
Miller & Chapman paper:
Miller GA, Chapman JP (2001) Misunderstanding analysis of covariance.
Journal of Abnormal Psychology 110:40-48.
So, depending on your data, it may be that there's no easy solution to
disentangling the effect of condition from the effect of response
time. If others have more helpful suggestions, I'll be interested to
hear them though.
Good luck!
Best regards,
Jonathan
--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/
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