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Re: st: time in xtmixed
David Hoaglin <email@example.com>
Re: st: time in xtmixed
Mon, 25 Mar 2013 15:33:20 -0400
Those results are a bit unusual. They suggest a need for a close look
at the data. (I have not seen information on the number of
observations or the nature of depvar or indepvar or, as mentioned in
another comment, the units of time.) You might, for example, plot
depvar vs. indepvar separately for each of the three time points, and
also look at how indepvar is related to time.
My rough interpretation is that the contribution of time is not
linear, so specifying time as categorical adjusts more effectively for
that contribution and leaves less for indepvar to account for. It is
not out of the question (obviously, since you see it in your data) for
time to have a (nearly) significant trend without either the time1 or
the time2 effect being significant. Those effects reflect the
contribution of time to depvar after adjusting for the contribution of
indepvar, so examination of the relation between indepvar and time is
likely to be important.
Have you changed your models? The models in your first message did
not include an interaction between indepvar and time.
I'm not sure what you mean by "the interaction between indepvar and
time ... at time 0." Even if your models include an interaction
between indepvar and time, the contribution at time 0 would be part of
the constant term (unless you center time).
If you went with option a, it would not be appropriate to say that,
independent of time, depvar and indepvar are associated. The
appropriate statement would be that, after adjusting for the linear
effect of time, depvar has a significant slope against indepvar. The
suggestion that the contribution of time is not linear, however, may
mean that even that more-careful statement is not a good summary of
Since you have only three time points, it will probably not be useful
to consider polynomials in time. Linear time is already the
first-order polynomial, and a quadratic in time would fit three time
On Mon, Mar 25, 2013 at 9:12 AM, megan rossi <firstname.lastname@example.org> wrote:
> How is this for confusing, when time is as a continuous variable (ie. option a)the association between depvar and indepvar is significant (time is borderline sig p=0.055), however as a categorical variable, option b, the relationship between depvar and indepvar becomes insignificant...and time 1 and 2 become very insignificant (p=0.55 and 0.17)
> In both scenario's the overall significance of the model is <0.0001 and log likelihoods are the same. The interaction between indepvar and time is only significant at time 0 not 1 or 2.
> If I did go with option a, could I really say that independent of time depvar and indepvar are association?
> Megan Rossi APD
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