A student questionnaire contained the question "Are you currently an active user of the on-campus gym?" (Yes/No).
Time series about gym use over the last 2 years are available for each student.
Some students answered "yes", although there was no evidence from the 2-year data, whereas others said no despite the data showing they used the gym very recently.
I want to see how well the usage data predicts the binary responses (logistic regression model).
So I derived the following possibly relevant variables:
- The number of uses in the last 1 year (n)
The duration (d) since the most recent use could be a good predictor. But it can be 0 if the gym was used in the same day of self-report, or missing if the data doesn't have usage records for the student.
response ~ d + n
Is there any methodological and/or statistical problems with this approach?
Do you think other more relevant variables can be derived from the usage data, or other methods that better utilizes the time series nature of the usage data?