CoolData has been quiet over the summer, mainly because I’ve been busy writing another book. (Fine weather has a bit to do with it, too.) The book will be for nonprofit and higher education advancement professionals interested in learning how to use multiple regression to build predictive models. Over the next few months, I will adapt various bits from the work-in-progress as individual posts here on CoolData.
I’ll have more to say about the book later, so if you’re interested, I suggest subscribing via email (see the box to the right) to have the inside track on this project. (And if you aren’t familiar with the previous book, co-written with Peter Wylie, then have a look here.)
A generous chunk of the book is about the specifics of getting your hands dirty with cleaning up your messy data, transforming it to make it suitable for regression analysis, and exploring it for interesting patterns that can strengthen a predictive model.
When you import a data set into Data Desk or other statistics package, you are looking at more than just a jumble of variables. All these variables are in a relation; they are linked by patterns. Some variables are strongly associated with each other, others have weaker associations, and some are hardly related to each other at all.
What is meant by “association”? A classic example is a data set of children’s weights, heights, and ages. Older children tend to weigh more and be taller than younger children. Heavier children tend to be older and taller than younger children. We say that there is an association between age and weight, between height and weight, and between age and height.
Another example: Alumni who are bigger donors tend to attend more reunion events than alumni who give more modestly or don’t give at all. Or put the other way, alumni who attend more events tend to give more than alumni who attend fewer or no events. There is an association between giving and attending events.
This sounds simple enough — even obvious. The powerful consequence of these truths is that if we know the value of one variable, we can make a guess at the value of another, as long as the association is valid. So if we know a child’s weight and height, we can make a good guess of his or her age. If we know a child’s height, we can guess weight. If we know how many reunions an alumna has attended, we can make a guess about her level of giving. If we know how much she has given, we can guess whether she’s attended more or fewer reunions than other alumni.
We are guessing an unknown value (say, giving) based on a known value (number of events attended). But note that “giving” is not really an unknown. We’ve got everyone’s giving recorded in the database. What is really unknown is an alum’s or a donor’s potential for future giving. With predictive modeling, we are making a guess at what the value of a variable will be in the (near) future, based on the current value of other variables, and the type and degree of association they have had historically.
These guesses will be far from perfect. We aren’t going to be bang-on in our guesses of children’s ages based on weight and height, and we certainly aren’t going to be very accurate with our estimates of giving based on event attendance. Even trickier, projecting into the future — estimating potential — is going to be very approximate.
Still, our guesses will be informed guesses, as long as the associations we detect are real and not due to random variation in our data. Can we predict exactly how much each donor is going to give over this coming year? No, that would be putting too much confidence in our powers. But we can expect to have plenty of success in ranking our constituents in order by how likely they are to engage in whatever behaviour we are interested in, and that knowledge will be of great value to the business.
Looking for potentially useful associations is part of data exploration, which is best done in full hands-on mode! In a future post I will talk about specific techniques for exploring different types of variables.
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