Data Analysis with SPSS (3rd Edition)
by Stephen Sweet and Karen Grace-Martin

Well, of course. It assumes you’ve never taken or done statistics before, so is at a very basic level. It shows how to do all analyses using SPSS, rather than by hand. For someone either just learning or reviewing statistics and learning SPSS. It is aimed at sociology majors, but even my mycologist (fungus biologist) neighbor found it helpful when she needed to review statistics for a job interview. If you’re pretty good at statistics and just want to learn SPSS, I recommend Green & Salkind.

Survival Analysis Using SAS: A Practical Guide
by Paul Allison

If you need to use Survival Analysis for the first time (or the 20th), you need this book, even if you don’t use SAS.  I just can’t say enough about how good it is.

Applied Linear Statistical Models
by Michael Kutner, John Neter, Christopher Nachtsheim,  & William Li

This is a textbook, through and through, but an excellent one.  It really covers just about everything in linear models (regression and anova), and it quite straightforward.  A great reference book.

Design and Analysis: A Researcher’s Handbook
by Geoffrey Keppel, William Saufley, & Howard Tokunaga

A fabulous ANOVA text. One of the best explanations of simple effects I’ve ever seen. I used this in an ANOVA class as an undergrad psychology major. It was challenging then, but is definitely accessible to social scientists. This is a true textbook and contains lots of exercises for computing statistics by hand (skip those parts). But it is a great reference book.

Design and Analysis of Experiments
by Douglas Montgomery

Another excellent ANOVA text. This one was written for engineering students, so it’s a bit more theoretical and mathy than Keppel. Definitely a compute-by-hand textbook, but is another fabulous reference.

Applied Linear Statistical Models
by Michael Kutner, John Neter, Christopher Nachtsheim, & William Li

This is a textbook, through and through, but an excellent one.  It really covers just about everything in linear models (regression and anova), and it quite straightforward. A great reference book.

The picture will take you to a previous edition, which will save you about $100 off the latest.

Regression Models: Censored, Sample Selected, or Truncated Data
by Richard Breen

This is a pretty specific topic, but it’s a great book when you need it. I love all of the books in this series that I’ve read. Contains enough statistical theory to be helpful, but not lots of technical jargon or deriving equations. Readers should be quite familiar with linear regression.

Logistic Regression Using the SAS System : Theory and Application
by Paul Allison

You’ve probably noticed by now that I really like Paul Allison’s books. He is one of my favorite applied statistics authors. This one is great as well. This book, combined with Scott Menard’s, are all you need to learn and use Logistic Regression.

A Step-by-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modeling
by Larry Hatcher

This is THE book to read if you’re doing Factor Analysis. The first chapter alone on Principal components is amazing.  Very well written and nontechnical. Even if you don’t use SAS, this book explains Factor Analysis VERY well. Just ignore the SAS code.

Regression Models for Categorical Dependent Variables Using Stata
by J. Scott Long and Jeremy Freese

This is the book that I used when I took my first course on SAS during graduate school, although that was many editions ago. It explains both the logic and the command language of SAS in a very understandable way. It’s one I refer to often.

An excellent resource book—it has a lot of little tips and tricks that really help you get to know SAS. For both beginners and advanced users.