Not clear from your message, but there are two likely issues. One is
that repeating items may be confounded with other manipulations*. In
that case the design is problematic. If not you can include
"animation" as a fixed effect in the model. This may improve the
senssitivity of your tests.

The effects will generalize only to those animations used in the
study - if you wish to generalize beyond these you need to treat
"animation" as a random effect being sampled.

Thom

* this can be avoided by counterbalancing or randomizing the
occurance of the animations.

Harold Hill wrote:
> 
> I wish to us a repeated measures design to investigate the effects of
> two types of exaggeration on the perception of animations.  For both
> types of animation I would like include a level that leaves the
> orginal unchanged.  Thus some trials would involve presentaion of
> identical animations but be in different cells of the design.  I guess
> my default approach would be to ignore this fact, except perhaps to
> plan to test that there is no difference between the equivalent cells.
>  However it seems you should be able to take advantage of the repetion
> either 1) By not duplicating the trials but using the same data for
> both cells of the design thus saving your observer's some trials.  I
> guess this invalidates the default model of sources of variance 2)
> Include duplicate trials, but in some way use the fact they are
> equivlaent to give you a better estimate of the variance associated
> with the equivalent cells.  Not sure how you would do this though.
> I hope this is a vaguely interesting question for this group and I
> would much appreciate any thoughts/references.  I can't believe I am
> in a unique situation as no treatment must be a fairly common level to
> want in for a variety of variables but it is not something that I have
> seen covered. Please note I am not a statistician but a humble
> psychologist - please tailor any answers appropriately.  This is what
> also makes me baulk at trying to implement an explicit model of
> sources of variances for this situation though I guess this is what
> you may recommend.
> Thanks,
>   Harold
.
.
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