Creating Variables: Retention and Attrition
Now that we’ve highlighted some basic variables in our Creating Variables series, I think we’re ready to move on to a variable that is a little trickier to create: retention. The retention variable we’ll create will represent whether or not a freshman is retained from one fall semester to the next. Because of the number of different factors you can choose to include or exclude, I hope that you’ll use these instructions as a general guide rather than a hard-and-fast manual on how to create a retention variable.
In order to create a fall-to-fall retention variable, we need to be looking at enrollment data from several consecutive fall semesters. In order to focus just on freshmen, we’ll start by placing a filter on our first semester:
Within the filter, you’ll want to focus on the field in your dataset that represents student year and filter down to just those students who are freshmen. If you’d like to include other filters here, such as making sure you’re focusing on first-time freshmen rather than transfer students, or undergraduate rather than graduate students, this is the time to do so.
The next step is to merge this dataset and filter with the next consecutive fall semester:
Inside the merge, you’ll want to connect the datasets on an identifying field, such as student ID. In doing so, you’ll want to take all of the information about each student from only the first column, so your merge should look something like this:
Here’s where the magic happens. After you’ve set up the merge, you need to do one more step before exiting the merge node. See the section titled “Source Table Flag Columns” in the bottom right corner? That’s where we’re headed. This section allows you to create a flag variable that tells you which dataset your information came from. In this case, we’ll create a flag to let us know whenever a student shows up in both datasets and we’ll call this variable “Retained” (double click on the existing name to rename it). It should look something like this:
Now we’re getting close, but we’re not done yet: those of you who have done some predictive modeling probably know that usually when we talk about retention, we’re actually planning to model attrition. (So, instead of modeling each student’s likelihood of leaving, we’d model each student’s likelihood of staying.)Modeling attrition rather than retention is helpful because we’re focusing on a smaller population of students and honing in on the characteristics of students who are likely to leave more directly. To accomplish this, we’ll need to add a transform in order to create a new attrition variable.
-Caitlin Garrett, Statistical Analyst at Rapid Insight
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