CoolData blog

Books, Blogs and White Papers

From time to time I will add books and other resources that I’ve found helpful. Suggestions for additional titles are welcome (comment below), but I list only those things I’ve actually reviewed and used. These are not affiliate links. (NOTE: A more general list of blogs and sites is contained in the blog roll, found on the lower right-hand side of any page on this site.)


Data Mining for Fundraisers by Peter B. Wylie. This is the book that started it all for me. With no background in statistics, I was able to say, “Yes, I can do this” as a result of reading this. A slim, pithy volume which you’ll want on your desk, because you’ll read it more than once.

Fundraising Analytics: Using Data to Guide Strategy by Josh Birkholz. This book will spark plenty of ideas to take you in various directions with your data work. My copy is full of Post-It notes.

Statistics in a Nutshell: A Desktop Quick Reference by Sarah Boslaugh and Paul Andrew Watters. I’ve long valued books in the O’Reilly series for their quality (and those covers are so cool). Definitely not “for dummies,” they are written in an intelligent but approachable way that appeals to the serious reader who may not be an expert. This great overview of the major statistical tools is no exception.

Applied Multiple Regression / Correlation Analysis for the Behavioral Sciences by Jacob Cohen and other authors. A used copy of this weighty textbook will set you back more than a few dollars, and it’s not light bedtime reading, so I don’t recommend this as a must-have for the beginner. But it may be the most exhaustive tome on applying regression in the non-physical sciences out there; I keep it handy for reference and deep insight into any particular aspect of regression I might be interested in.


Prospect-DMM – A discussion group for development professionals involved or interested in data mining and modeling.


KDnuggets – All things data mining and knowledge discovery. Sign up for their free twice-monthly email newsletter.

List of Data Mining Blogs – Plenty of further reading here, compiled by the blog Data Mining Research.

Supporting Advancement – A page of data, reporting, and data mining resources for people working in university advancement.

FlowingData – For people who love data and visualizing data. FlowingData explores how designers, statisticians, and computer scientists are using data to understand ourselves better – mainly through data visualization.

Jennifer Schmidt-Olomon – Jen Olomon is a full time MSIS student with a particular interest in business intelligence applications for nonprofit fundraising. On her blog she posts updates on her research, drafts of papers, and related interesting stuff.


Data Desk – An intuitive stats software package that even a journalism grad like me can use – but powerful, too.

White papers and downloadable discussion briefs

Elliotte, Debra – Data Mining in Higher Education – Master’s degree project, 2010. An analysis which identified characteristics of non-donors most likely to be acquired as new donors to the state college Marshall University, using the modeling software Rapid Insight.

Lindahl, Wesley E., and Christopher Winship – Predictive Models for Annual Fundraising and Major Gift Fundraising – Predictive modeling for fundraising is NOT new. Everything that might seem to us to be on the cutting edge has already been written about in this excellent chapter from 1992 (reproduced from Nonprofit Management & Leadership).

Luperchio, Dan – Data Mining and Predictive Modeling in Institutional Advancement: How Ten Schools Found Success – Dan Luperchio of The Johns Hopkins University was the first holder of the CASE Peter B. Wylie Data Mining Internship, in 2008. One of the outcomes of his eight weeks of internship was this technical report, produced in partnership by the Council for Advancement and Support of Education (CASE) and SPSS Inc. The report explores the promise of data mining alumni records at educational institutions.

Wylie, Peter B. – A Simple Score – “What I want to do here is talk about how (with the help of your IT folks) you can combine five basic pieces of information into a score that will help you save money and generate more revenue – especially if you work with the annual fund.”

Wylie, Peter B. – Are Donor Dollars Related to How Long Their Names Are? – The hunt for predictors that work across donor databases.

Wylie, Peter B. and John Sammis – Does Data Mining Really Work for Higher Education Fundraising? – A study of the results of predictive models built for five higher education institutions.

Wylie, Peter B. – Getting to Know Your Online Donors Can Pay Off – “Who are our online donors? How are they different? Are online donors better givers – more generous and reliable – than other donors? A statistical analysis provides answers.”

Wylie, Peter B. – Can We Predict Pledge Fulfillment Rate for the Annual Fund? – A case study using 10,000 records from a higher-education institution’s phone campaign.

Wylie, Peter B. – Sports, Fund Raising, and the 80/20 Rule – The white paper whose title led to a book. “It made sense to me that about 80 percent of the money raised from any given database would have been contributed by about 20 percent of the records in that database. But I never got around to systematically checking out my hunch.”

Wylie, Peter B. – Where the Alumni Money Is – Gathering the evidence to show that if you’re not mining your institution’s database, you’re leaving money on the table.

1 Comment »

  1. Finding your blog very helpful, Kevin. I saw your book recommendation of Applied MRC Analysis for Behavioral Sciences, and it relates to a decision I’m trying to make regarding a class to take. The class is called Statistics for Behavioral Sciences. I am the donor database manager for Big Brother Big Sisters of Massachusetts Bay, and I am eager to try some predictive modeling and other analysis on the data. Would things I learn in such a class be applicable to fundraising or is the focus on behavioral sciences be something that would render this class unproductive for me? Here is how the instructor describes the class:

    “For this class, we don’t do much probability. The main focus is organizing data (categorical data, numerical data, etc)in a meaningful ways –using tables, graph, formula, etc. We will also study variety of distributions. Then we focus a lot on normal distribution and understands what the info normal distribution can give us. Finally, we do some regression study of relations of one quantity depending on other other quantity.”

    Predictive modeling is something the instructor does not cover, but I think I can uncover other sources to help me with that. I want to learn more about basic statistics and apply them to my profession.

    Comment by lynnsideedition — 16 August 2011 @ 10:38 am

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