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Preface

This little book isn’t designed to be a definitive textbook on multivariate statistics.  It isn’t even designed to be a textbook at all. It is designed to be a sort of guide, a plain English foray into an often complex and enigmatic field of endeavor.  Make no mistake: Multivariate statistics can be mind-numbingly hard. There is matrix algebra, iterative processes, and a multitude of other things that normal people with normal brains can’t possibly understand.  Lucky for us, we have computer scientists, statisticians, mathematicians, and other numerical gurus to program software to do all that heavy lifting for us. We, the humble social scientists of the world, are all about humans and theories about humans.  Numbers generally don’t excite us. Ideas, however, should excite you. If not, why on earth did you go to graduate school in the first place?! In this text, we’ll focus on ideas and overarching concepts (if you really want to know about the numbers, see the “References and Further Reading” at the end of each Section).

I’ve made the conscious decision to abandon some of the normal conventions of scholarly writing to keep this thing as informal and conversational as possible.   Since most of us that read this book will be doing it for a class, I’ve decided to refer to “this class” in reference to the body of material that we’ll be talking about.  I think that makes things a little more approachable; just keep in mind that if I am not your professor, then I don’t get the final word. Also keep in mind that in trying to make this easier to understand, I’ve resorted to some simplifications that may well cross over into oversimplifications.   I am moving forward under the premise that the best first step is to grasp the concept, then worry about the fine print. I’ll refer you to some real books and articles written by respectable scholars that you can depend on to correct the oversimplifications. For now, I’ll leave you with a simple rule for moving forward:

DON’T PANIC.

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