Using Stata 11.2, I would like to develop 2 analytic models that could be implemented by school administrators to flag students for intervention. I'm wondering if it would be possible to develop these models based on historical cross-sectional data consisting of 650,000 unique observations (11th grade students); it was collected from 2009 through 2011.
First, I want to predict risk of dropping out of school (with the goal of intervening in 5% of students with the highest risk of dropout). Second, I want to predict absence (with the goal of intervening among students in the top 5% of predicted absentee time).
Outcome variables: dropout (binary; 0=no, 1=yes) and absence (continuous; cumulative hours absent from school)
Predictor variables: sex (binary), track (3-categories), GPA (continuous)
So far, I've just worked on predicting dropout, but am not sure if what I'm doing is theoretically correct or statistically sound. (It's been a long time since I took stats!) Here what I've done, using the Stata command:
logistic dropout i.sex i.track gpa
The output shows all independent variables are significantly associated with dropout, based on p<0.05 and 95% CIs for the ORs that do not contain 0. Sex (OR=0.95), Track (OR=0.76), GPA (OR= 1.52).
Now I'm not sure about how to proceed in calculating the predicted probabilities of dropout, and then figuring out which students are at greatest risk of dropout. Should the command be
predict phat ?
I think this gives the predicted prob of dropout for each level of each variable, holding all other variables at their mean. Then I would just categorize the predicted values into 20 categories, and the students in the top category would be those who should be targeted for intervention?
I would greatly appreciate recommendations on how to conduct both analyses (binary and continuous outcomes).