Multiple Imputation With a Monotone Missing Data Pattern

In longitude data analysis, we call this missing data as a monotone, missing Miss Python. So that means once you drop out, they never come back. We can construct waves by waiting back to the previous wave. And we can also do multiple implementation because you can include many variables in your imputation model. It could also be an upgrade in some, in many cases, I guess. Yeah.