How to include 29 colleges as an independent variable in logistic regression I am working on a model that predicts gestation period of new hires. By gestation period, I mean the period between when they completed their training and the time they start working on their first real live project. I have data for gestation period in months. I classified the time into two parts - "0 - 3 months" and "3 months or more". The purpose of the model is to reduce gestation time. It means the HR will recruit from only those colleges in which the students had performed well in the past and they went live early (i.e., low gestation time). 
I have run logistic regression using gestation time as dependent variable - 1 for "0-3 months" and 0 for "3 months or more".
The independent variables are their college names, education qualification, specialization subject, graduation scores, training scores etc. Under the college name variable, I have data for 29 colleges. In other words, there are 29 options in this variable. How can I use this information as an independent variable in developing a predictive model? Should I take "1-29" options? Or is there any other way to group the data for this variable in logistic regression? Are there any other statistical technique you would suggest?
 A: A few points


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*This sounds like a survival analysis/event history analysis model (e.g. discrete time survival analysis, Cox proportional hazards model, etc.) because you are measuring time to event (time to working on first project). Such models are described as explaining the probability of whether and when an event will occur.

*It also sounds like it is a mixed model because of the colleges.
These two points are quite compatible, as the hierarchical data format required for event history analysis is more or less precisely what is needed for mixed models as well. My suggestion would be to nest individual employees within colleges, so you might have (in the discrete time case) a college-person-period data format like this:
College   PersonID    Period    FirstProj  Covariates
1         1           1         0
1         1           2         0
1         1           3         1
1         2           1         0
1         2           2         1
2         3           1         0
2         3           2         0
2         3           3         0
2         3           4         0
2         3           5         0
2         3           3         1

The first three variables are identifiers of the factor/unit at-that-level defining the data hierarchy. Notice that these variables are sorted which is necessary to do explicitly in many statistical software packages for these kinds of analyses. Note also the the variable Period is not calendar time, but time that the subject was observed so the beginning of study time (perhaps in work days, or in weeks, depending on your needs) corresponds to the value 1 for every subject, even though they were began training/were first observed on different calendar dates.
The College-level residuals will tell you which colleges are at either extreme. Such models will also permit college, individual, and period-level (time-varying) predictors.
