Imbalance in observable data I am studying the performance, over 10 years, of high school students that enrolled in a school district. My Objective is to make inferences about factors leading to poor performance in school exams. I have data and stats on all those who were dropouts. Drop outs are a big group from among the poor performers. For example if there are 1000 students, 250 are poor performers. Of these 250, 200 are drop outs and there is no data on those. I plan to run a logistic regression model and draw inferences, but how do i remedy the imbalance that this missing data is creating?
So You have 750 : 50 ratio of positive and negative class. Try running LR on this data. Class imbalance would not be a problem. Even if so, you under sample the positive class and then re-calibrate the probabilities after model execution.
I would also suggest you to be active on cross-validation stack-exchange community. There are similar questions answered.