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.

In the transform, we’re going to set up the attrition variable to be the opposite of the “retained” variable. To do this, we’ll want to select the “retained” variable to work with and type the following into the Enter a Formula window: IF (A=1, 0, 1). This basically turns zeros to ones and ones to zeros. Set the result type to binary and be sure to name the variable something like “attrition” before creating and exiting… And there you have it, a state-of-the-art attrition variable to add to your collection!

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Rapid Insight's Summer Reading List

With June fast approaching, summer has been on our minds here at the Rapid Insight office. Since it’s a higher education tradition to assign summer reading material, we decided to compile our own summer reading list for our customers (and we promise you won’t be tested on it!). Some of these books are more analytical than others, but they all come recommended by Rapid Insight staff. 

11/22/63 by Stephen King (John Paiva, Account Manager)

            Jake Epping, a 35-year-old high school English teacher, is enlisted on a mission to try to prevent the Kennedy assassination by traveling back in time to 1958. In this case, the seemingly obvious decision to deter Lee Harvey Oswald may not be the best or most obvious decision – but spurs you to think about what you’d do in the same situation. 

Blink: The Power of Thinking without Thinking by Malcom Gladwell (Sheryl Kovalik, Director of Operations)

            Blink is all about how we think without thinking, and the choices we make instantaneously every day. From meeting someone for the first time, to reading the first few sentences of a new book, to walking into a house you’re thinking of buying, Blink explores the split-second instant conclusions we reach and why’re they’re powerful.


Cultivating Data-Based Decision in Higher Education: Using Rapid Insight, Inc. Veera by Scott Mantie
             This book was written by one of our customers, Scott Mantie, about how to address the challenges of higher education and make meaningful data-based decisions using Veera. It provides an overview of Veera, but rather than attempting to be an in-depth reference manual, Mantie's book is meant to motivate novices to use Veera, as well as to expose more seasoned users to real-world examples and tips. 

Innumeracy: Mathematical Illiteracy and its Consequences by John Allen Paulos (Caitlin Garrett, Statistical Analyst)

            Innumeracy addresses the divide between our society’s dependence on math and science and the number of people who are “bad with numbers”. By using witty and relevant anecdotes to explain basic topics in probability and statistics, Innumeracy seeks to educate those who aren’t so comfortable with math.

The New Direct Marketing by David Shepard Associates (Mike Laracy, President and CEO)

            Mike recommends this book to anyone looking to learn the basics of analytics for direct marketing. This book covers how to combine your database information with analytical methods to identify and profit from your existing customer base, as well as provides strategies for acquiring new customers. 

Regression Analysis: Statistical Modeling of a Response Variable by Rudolf J. Freund and William J. Wilson (Mike Laracy, President and CEO)

            For anyone interested in learning the fundamentals of statistical modeling, Mike recommends this book and says it provides a great foundation in the skills and techniques used for predictive modeling and regression analysis.

The Richest Man in Babylon by George S. Clason (Steve Matthews, Software Engineer)

            Now heralded as a modern-day classic, this book uses ancient Babylonian parables to make points about finances and wealth. Clason’s financial advice is timeless, and this book is easy to read for both seasoned investors and newcomers alike.

Tick, Tock. by James Patterson & Michael Ledwidge (Chris Major, Sales Associate)

            Michael Bennett, a NYC detective, is put on the case following a series of attacks thought to be a terrorist activity. The trail leads Bennett and the FBI to a startling revelation on the true origin of the murder spree... Chris says he tore through the first few hundred pages, and that this book was an “absolute page-turner” and a “thriller”. 

Unbroken: A World War II Story of Survival, Resilience, and Redemption by Laura Hillenbrand (Tricia Mills, Account Manager)

            During World War II, an Army Air Forces bomber crashed into the Pacific Ocean and disappeared, but there were three survivors, including Louis Zamperini, a lieutenant and former Olympic runner. Unbroken is his gripping story of resilience and courage. Tricia describes the story as “awesome” and it made her realize how much she didn’t know about the WWII period in history.

Unholy Night by Seth Grahame-Smith (Jim Durdan, Sales Associate)

            From the author of Abraham Lincoln: Vampire Hunter and Pride and Prejudice and Zombies, comes Unholy Night, which is another dark revision of history. This time focused on the Nativity, Grahame-Smith’s version of that scene involves the three kings actually being master thieves and great evils roaming the holy land.

The Way of the Peaceful Warrior: A Book That Changes Lives by Dan Millman (Julie Crawford, Account Manager)

            Dan Millman, a world-champion gymnast, met his mentor (nicknamed Socrates) during his junior year of college, and embarked on a spiritual journey to learn “the way of the peaceful warrior”. Together, they maximized Dan’s performance by combining Western fitness training with Eastern philosophy. Julie read this book to her teenage boys, who liked it a lot.

... If you  have any summer reading suggestions for us, please leave them in a comment!

UB Readers' Choice Awards

As you may know, University Business magazine is launching its first annual Readers’ Choice Top 100 Products award this year. This award will be given to 100 products used in higher education based on nominations from its readers. Winners will be selected based on both the quantity and quality of reader nominations, and will be featured in a special issue along with testimonials describing the impact each product has had.

Here at Rapid Insight, we're working hard to make data analysis as easy as possible for our higher ed customers. Whether that means data clean-up, reporting, or modeling, we hope we're making a difference in the way you work with your data. You've told us time and again how our products have saved your analytic life!  This is a great opportunity for you to tell others.  If we’ve made a difference in your analytic world we’d greatly appreciate your nomination.

Creating Variables: Out-of-state Flag

Sometimes it’s good to see which of your students or donors are in-state because an in-state population may be more likely to enroll or be retained or give than an out-of-state population. Creating an out-of-state flag from a “state” variable allows you to easily differentiate between your in-state and out-of-state prospects. I should also note that it is just as easy to create an in-state flag if that better suits your data. In any case, here’s how:

The first step is to hook your data source to a transform node:

Because we’ll be creating a binary (“yes or no”) variable, we’ll want to click on the “if” button (at the top of the buttons on the right side), which will automatically generate an equation that we can change to suit our data. 

In the “Enter a Formula” window, we’ll want to edit the auto-generated equation so it reads:

Where ‘[A]’ is the variable in our dataset that represents state, and the term it is set equal to (in this case, ‘NH’) is the term in our dataset that represents our institution’s state. Note that we could have set state equal to ‘New Hampshire’ or a numerical code, as long as it matches the term that represents New Hampshire in our dataset. The equation outputs a variable that is equal to ‘1’ when state is NOT New Hampshire and ‘0’ otherwise, thus flagging records which are out-of-state.

The final step before naming and saving your out-of-state flag is to select “binary” from the “Result Type” list.

And, voila, it’s easy as that! You now have a quick way of identifying in-state vs. out-of-state students in your dataset; let the reporting begin!

PS: If you guys have any specific requests for a variable to be featured in the "Creating Variables" series, please leave them in the comments or email me directly!

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Creating Variables: Age

Hi all! Today I’d like to a cover a pretty universally predictive variable: age. Age can be created in relation to the date of a particular event (like an application date or a mailing date), or as a reflection of age today, at this moment. Either way, age is often predictive and easy to add to your dataset by creating it in Veera from a “birth date” field.

The first step in doing so is to hook your data source to a transform node: 

After opening the transform node, we’ll want to click on the function button and select the second “YearsBetween” function.

[Note: Veera is capable of outputting the number of years between two dates in two separate ways. The first function on the list calculates the number of years between two dates, regardless of the actual day and month, while the second function calculates the number of years between two dates taking day and month into account. To illustrate this point, take the dates December 1, 1960, and April 1, 1980. Using the first “YearsBetween” function, the number of years between these dates is 20. Using the second “Years Between” function, the number of years between these dates is 19. See the difference?]

Here, we have two options. We can (a) calculate age today or (b) calculate age at a specific point in time, depending on what we type in the “Enter a Formula” window.

(a) Age today:  

Where ‘[A]’ corresponds to the variable in your dataset that represents birthdate, and “TODAY()” is the Today function from the drop-down menu on the right. 

or

(b) Age at a specific point in time:

Where ‘[A]’ corresponds to the variable in your dataset the represents birthdate, and ‘00/00/0000’ represents the specific date on which you’d like to measure age. 

Be sure to save before exiting the transform node, and there you have it, a brand-new age variable!

PS: If you guys have any specific requests for a variable to be featured in the "creating variables" series, please leave them in the comments or email me directly!

-Caitlin Garrett, Statistical Analyst at Rapid Insight