A colleague has a data set with a structure like the one below:
ID X1 X2 Y
1 1 0.70 0.40
2 1 0.80 0.40
3 1 0.65 0.40
4 2 1.20 0.25
5 2 1.10 0.25
6 3 0.90 0.30
7 4 0.50 0.50
8 4 0.60 0.50
9 4 0.70 0.50
Where X1 is the organization. X2 is the percent of market salary an
employee within the organization is paid--i.e. ID 1 makes 70% of the market
salary for their position and the local economy. And Y is the annual
overall turnover rate in the organization, so it is constant across
individuals within the organization. There are different numbers of
employee salaries measured within each organization. The goal is to assess
the relationship between employee salary (as percent of market salary for
their position and location) and overall organizational turnover rates.
How should these data be analyzed? The difficulty is that the data are
cross level. Not the traditional multi-level model however. That there is
no variance across individuals within an organization on the outcome is
problematic. Of course, so is aggregating the individual results. How can
this be modeled both preserving the fact that there is variance within
organizations and between organizations. I suggested that this was a
repeated measures problem, with repeated measurements within the
organization, my colleague argued it was not. Can this be modeled
appropriately with traditional regression models at the individual level?
That is, ignoring X1 and regressing Y ~ X2. It seems to me that this
violates the assumption of independence. Certainly, the percent of market
salary that an employee is paid is correlated between employees within an
organization (taking into account things like tenure, previous experience,
etc.).
Thanks
=================================================================
Instructions for joining and leaving this list and remarks about
the problem of INAPPROPRIATE MESSAGES are available at
http://jse.stat.ncsu.edu/
=================================================================
