I am running a logistic regression model with the following data and variables:
Independent variables (characteristics of a person coded as dummy)
- Test score (continuous variable between 0 and 1)
- Education (dummy variable for Ivy league degree; categorical variable for type of major)
- Age (discrete variable)
Dependent variable (person's performance at task)
- Task is to select a company that will be successful at given point in time (success is coded as dummy variable: 0: fail, 1: success -- based on a definition from the specific literature)
I want to run the regression such that each combination of person-task (task meaning company selected) is one observation.
There are 2 issues I am not sure how to account for in the model:
Most persons completed a different amount of tasks at different points in time (some selected only 5 companies, others 60), thus I am concerned that those with more observations will be weighted more heavily.
Some persons selected the same companies, thus they share the same outcome (success or failure) and hence, I am concerned that some outcomes are overweighted if a company was chosen several times by different persons (one person cannot choose the same company more than once).
How can I account for this in the logistic regression model? I have been looking at using weights and clustering standard errors, but I am not sure if this is entirely correct.
Thank you in advance for your help!