Dear Saul,
The most commonly used mixed-effect models software in R, in the lme4 and nlme
packages, use the Laird-Ware form of the model, which isn't explicitly
hierarchical. That is, higher-level variables are simply invariant within groups
and appear in the model formula in the same manner as individual-level
variables. So there's no problem -- just specify the model as you normally
would.
By the way, you're more likely to get responses about mixed models if you
post to the R-sig-mixed-models list
<https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models> rather than to
the more general R-help list.
I hope this helps,
John
-------------------------------------------------
John Fox, Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
Web: http::/socserv.mcmaster.ca/jfox
> On Mar 3, 2019, at 5:19 AM, Saul Weaver <saul.weaver.70 at gmail.com>
wrote:
>
> Hello,
>
> I have data with workers within departments. I am interested in testing the
> effects of peers' satisfaction on employees' productivity. To
assess peer
> satisfaction, I calculate, for each employee, the average satisfaction of
> the employees' peers within the department. In other words, I calculate
the
> average satisfaction in the department, while excluding the focal employee.
> I'm not sure about the level of this variable, because on the one hand,
it
> is unique for each employee, but on the other hand, the values of this
> variable across employees are not independent of each other. How would I
> account for this issue in R?
>
> Thank you,
>
> S Weaver
>
> [[alternative HTML version deleted]]
>
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