# Mixed Effects Logistic Regression w/ Repeated Measures for Observational Data

I am working on a study of how different scholarship programs at a university may influence student retention (i.e., if students are still enrolled at the university one year later). Each student can receive a different combination of scholarships--they can also lose or gain scholarships over time. All students in the study started in the same cohort/semester.

My data has the following variables:

• Student ID: identifier for each student
• Term: semester that the student had the scholarship (categorical)
• Award Type: whether scholarships are awarded based on merit, financial need, or special interest (categorical)
• Award Prop: the proportion of student expenses that the scholarship covers - for example, an Award Prop of 0.50 means that the scholarship will cover half of a student's college expenses (continuous)
• Outcome: whether or not the student persisted a year later (binary)

An example of the data is provided below:

ID     Term           Award Type   Award Prop   Outcome (Persisted?)
0025   Fall 2012      Merit        0.25         Y
0025   Fall 2012      Need         0.50         Y
0025   Spring 2013    Need         0.50         Y
1310   Fall 2012      Interest     0.30         N
1310   Spring 2013    Interest     0.30         N
1229   Spring 2013    Need         0.90         Y
1229   Spring 2013    Interest     0.10         Y
2843   Fall 2012      Merit        0.50         N


Due to the fact that I'm modeling a binary outcome for individuals sampled several times, I am thinking of approaching this with a mixed effects logistic regression with repeated measures using lme4 in R.

I think the way to structure this might be to nest ID within Term as a random effect:

glmer(Outcome ~ AwardType + AwardProp + 1|(Term/ID), family=binomial)


First, based on the parameters above, does this seem to be a sound way to approach this analysis?

Second, should I be concerned about potential issues with missing/unbalanced data (since each person has different scholarships, a person with a scholarship one semester may not have it in other semesters, etc.)? One article I read said that mixed effects modeling should take care of this.