Multilevel models
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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