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"The second nugget of hope comes from
the help file within xtmixed. In this file, the first example of a
random coefficients model is one where the coefficient that varies has
no level 1 variation."
Not sure what you mean by that. Your "level 1" is, in terms of -h xtmixed-,
the "fe_equation", i.e. the fixed-effects part of the model. And sure
enough, "grade" does appear there, so it is accounted for on your "level 1".
There is not supposed to be any variation there, though - hence the name
"fixed effects".
The first time variation for a coefficient even enters the picture is on
your "level 2", which the help file calls "re_equation", following the
double pipe symbol.
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected]
[mailto:[email protected]] Im Auftrag von Peter Goff
Gesendet: Donnerstag, 17. Dezember 2009 17:20
An: [email protected]
Betreff: st: xtmixed: baffling random component
Hi All
I'm working with a 2-level model (I have teachers within schools); I'm
using xtmixed to examine this nested model.
Upon a reviewer's insightful suggestion I added a level-2 covariate to
my model (tch_mean). Owing to my own insomnia, I mistakenly assumed
this was a level 1 variable and decided to check to see if there was
significant variation if I added a random component to this
coefficient into the model. Lo-and-behold there was. Now I've
regressed into a little ball of confusion trying to understand why
this is.
At first I thought I made a moronic mistake (which, I'm aware, may
still be the case). Two nuggets indicate I may not have. One, stata
didn't crash or kick out an error when I asked it to allow a level 2
variable to vary - so apparently it is computationally feasible
(though it may not be sensible). The second nugget of hope comes from
the help file within xtmixed. In this file, the first example of a
random coefficients model is one where the coefficient that varies has
no level 1 variation. The syntax they use is parallel to my own.
I'm investigating this point because the results generated with this
level 2 variation are much more interesting than without this
variation (it changed the magnitude and increased the significance of
some other level 2 variables). My current understanding of the random
component doesn't leave any room to accommodate how my situation can
be explained. I had thought that there needed to be variation within
the level 1 cluster to allow for a random component (and the level 2
variables are used to predict the school-specific intercepts). Any
thoughts you have would be greatly appreciated.
Here's the code from the example in the stata help file:
Setup
. webuse nlswork
Random-intercept and random-slope (coefficient) model, correlated
random effects
. xtmixed ln_w grade age c.age#c.age ttl_exp tenure
c.tenure#c.tenure || id: grade, cov(unstruct)
My code:
. xtmixed gap diff2 tdbkgd3a tch_mean sd pd_gender pdyradm pdyrtch
pdyrsch enroll_07 econdis_07 tcap_ssm_08 ///
|| prinid: tch_mean, mle cov(un) var
where prinid identifies schools.
I'm having trouble understanding why this is a methodologically sound
approach (if I'm correct in inferring this from the similar example
within the help file). How is giving a level 2 covariate a random
component understood within a 2-level model?
My second question will likely be explained through an understanding
of the above, but I'd also like to know why/how allowing this level-2
to have a random component so drastically changes my other level-2
variables (at least 3 of them). How do I interpret the other level-2
variables in light of the significant random variation of tch_mean?
Kind thanks for your insights,
~PG
Peter Trabert Goff
PhD student
Department of Leadership, Policy, and Organizations
Vanderbilt University
Peabody #514
230 Appleton Place
Nashville, TN 37203-5721
Tel. 615-415-7844
Fax. 615-322-6596
[email protected]
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