Dear Peter
I share your confusion about these particular examples. As I look at
the "nlswork" dataset, it seems that this data represents repeated
observations of women across years, with "idcode" representing the
identifier of the woman (the level 2 identifier) and "year" representing
the year of measurement of the woman within the year. Using "year" as a
level 1 variable (that varies within each woman), we could form a simple
random intercept model as
* Random intercept model
xtmixed ln_w year  idcode:
and then extend this to a random slope model, assessing the extent to
which the slope of "year" varies across women.
xtmixed ln_w year  idcode: year
Seeing that there is variation in the slope of "year" predicting "ln_w"
across women, we could then try to explain this variation, by, as you
suggested, introducing a cross level interaction. For example, perhaps
women who are college graduates have higher slopes that noncollege
graduates (i.e. the relationship between "year" and "ln_w" is higher for
college graduates than noncollege grads). We could try this model like
this...
xtmixed ln_w i.collgrad##c.year  idcode: year, cov(unstruct)
In fact, the results show exactly this result. The slope between
"year" and "ln_wage" is 0.049 for noncollege graduates, but for
college graduates, the slope is higher by 0.0056 (p < 0.001).

ln_wage  Coef. Std. Err. z P>z [95% Conf.
Interval]
+
1.collgrad  .0493273 .115747 0.43 0.670 .2761872
.1775326
year  .0175735 .0006167 28.50 0.000 .0163648
.0187822

collgrad#
c.year 
1  .0056061 .001491 3.76 0.000 .0026838
.0085284

_cons  .2003554 .0465266 4.31 0.000 .1091649
.291546

I hope this helps.
Best regards,
Michael N. Mitchell
See the Stata tidbit of the week at...
http://www.MichaelNormanMitchell.com
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Peter Goff wrote:
I have a question that pertains to one of the examples given in the
xtmixed help file. Using the twolevel data set "webuse nlswork" from
the first example in the help file, I see that the command:
xtmixed ln_w grade age c.age#c.age ttl_exp tenure c.tenure#c.tenure 
id: grade, cov(unstruct)
can be used to create a random coefficient model. However, the data
file itself shows that the variable grade does not vary at the highest
level (level 2), i.e. it is constant within id (level 1).
From a multilevel modeling approach I have interpreted random
coefficient models to mean that the slope (of grade, in this example)
for each cluster can have a different impact upon the dependent
variable (ln_w, here). Although within this context there is no
variation of grade within individuals so I'm not clear how to
interpret this model.
Taking this a step further, if the model included an interaction
between the level 2 variable and a level 1 variable such as:
xtmixed ln_w grade c.grade#c.age age c.age#c.age ttl_exp tenure
c.tenure#c.tenure  id: grade, cov(unstruct)
would this change the interpretation of the random component of grade?
Kind thanks,
~Peter
Peter Trabert Goff
PhD student
Department of Leadership, Policy, and Organizations
Vanderbilt University
Peabody #514
230 Appleton Place
Nashville, TN 372035721
Tel. 6154157844
Fax. 6153226596
peter.t.goff@vanderbilt.edu
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