I am trying to estimate how faculty salaries at my university are allocated
by instructional level and academic discipline to estimate the actual cost
of teaching a semester credit hour by instructional level and discipline. I
developed a regression model with faculty teaching purely in specific
academic disciplines as my observations. Actual salary is my dependent
variable. Independent variables include lower-level credit hours taught,
upper-level credit hours taught, masters-level credit hours taught, doctoral
credit hours taught, total students taught, and dummy variables for faculty
rank, tenure status, and academic discipline. My original idea was to
either:
1) use dummy variables for instructional levels, or
2) plug in lower-, upper-, and masters-level credit hour data one at a time
to get separate estimates for each level and discipline.
The dummy variable idea will not work, because I do not know what amount of
each salary is spent at each level for my dependent variable. And, I will
double count the estimators for tenure status, faculty rank, and discipline
if I just plug credit hour data for each of the different instructional
levels into the model while using zeros for other levels of instruction to
get a prediction for each level. Is there a way to predict the salary cost
by instructional level and discipline without fitting the model only on
faculty who are teaching purely in one discipline and purely at one
instructional level? I can do that, but I am concerned that such faculty
would not be very representative of the population. I am not too worried
about fitting the regression model to faculty who teach in a single
discipline, since most do this anyway, but am afraid that limiting to
faculty who teach at a particular level will skew the results.
Thanks!
Joe Meyer
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