Stocking the Data Analyst’s Bookshelf

Many years ago, when I was teaching in a statistics department, I had my first consulting gig. Two psychology researchers didn’t know how to analyze their paired rank data. Unfortunately, I didn’t either. I asked a number of statistics colleagues (who didn’t know either), then finally borrowed a nonparametrics book. The answer was right there. (If you’re curious, it was a Friedman test.)

But the bigger lesson for me was the importance of a good reference library. No matter how much statistical training and experience you have, you won’t remember every detail about every statistical test. And you don’t need to. You just need to have access to the information and be able to understand it.

My statistics library consists of a collection of books, software manuals, articles, and web sites. Yet even in the age of Google, the heart of my library is still books. I use Google when I need to look something up, but it’s often not as quick as I’d hoped, and I don’t always find the answer. I rely on my collection of good reference books that I KNOW will have the answer I’m looking for (and continually add to it).

Not all statistics books are equally helpful in every situation. I divide books into four categories– Reference Books, Software Books, Applied Statistics Books, and data analysis books. My library has all four, and yours should too, if data analysis is something you’ll be doing long-term. I’ve included examples for running logistic regression in SAS, so you can compare the four types.

1. Reference Books are often text books. They are filled with formulas, theory, and exercises, as well as explanations. As a data analyst, not a student, you can skip most of it and go right for the explanations or formula you need. While I find most text books aren’t useful for learning HOW to do a new statistical method on your own, they are great references for already-familiar methods.

While I have a few favorites, the best one is often the one you already own and are familiar with, i.e. the textbooks you used in your stats classes. Hopefully, you didn’t sell back your stats text books (or worse, have the post office lose them in your cross-country move, like I did).

Example: Alan Agresti’s Categorical Data Analysis.

2. Statistical Software Books focus on using a software package. They tend to be general, often starting from the beginning, and cover everything from entering and manipulating data to advanced statistical techniques. This is the type of book to use when learning a new package or area of a package. They don’t, however, usually tell you much about the actual statistics–what it means, why to use it, or when different options make sense. And these are not manuals–they are usually written by users of the software, and are much better for learning a software program. (I think of learning a software program like learning French from a French dictionary–not so good).

Example: Ron Cody & Jeffrey Smith’s  Applied Statistics and the SAS Programming Language

3. Applied Statistics Books are written for researchers. The focus is not on the formulas, as text books are, but on meaning and use of the statistics. Good applied statistics books are fabulous for learning a new technique when you don’t have time for a semester-length class, but you will have to have a reasonably strong statistical background to read or use them well. They aren’t for beginners. The nice thing about applied statistics books is they are not tied to any piece of software, so they’re useful to anyone. That is also their limitation, though–they won’t guide you through the actual analysis in your package.

Example: Scott Menard’s Applied Logistic Regression Analysis

4. Statistical Analysis Books are a hybrid between applied statistics and statistical software books. They explain both the steps to the software AND what it all means. There aren’t many of these, but many of the ones that exist are great. The only problem is they are often published by the software companies, so each one only exists for one software package. If it’s not the one you use, they’re less useful. But they are often great anyway as Applied Statistics books.

Example: Paul Allison’s Logistic Regression using the SAS System: Theory and Application

If you are without reference books you like, buy them used. Unlike students, you don’t need the latest edition. Most areas of statistics don’t change that much. Linear regression isn’t getting new assumptions, and factor analysis isn’t getting new rotations. Unless it’s in an area of statistics that is still developing, like multilevel modeling and missing data, you’re pretty safe with a 10 year old version.

And it does help to buy them. Use your institution’s library to supplement your personal library. Even if it’s great, getting to that library is an extra barrier, and waiting a few weeks for the recall or interlibrary loan is sometimes too long.

I have bought used textbooks for $10. Menard’s book, and all of the excellent Sage series, are only $17, new. So it doesn’t have to cost a fortune to build a library. Even so, paying $70 for a book is sometimes completely worth it. Having the information you need will save you hours, or even days of work. How much is your time and energy worth? If you plan to do data analysis long term, invest a little each year in statistical reference books.

The full list of all four types of books Karen recommends is on The Analysis Factor Bookshelf page.

If you know of any other great books we should recommend, comment below.  I’m always looking for good books to recommend.